Merge pull request #3864 from svlandeg/feature/nel-wiki

Entity linking using Wikipedia & Wikidata

bin/ud/conll17_ud_eval.py

@@ -292,8 +292,8 @@ def evaluate(gold_ud, system_ud, deprel_weights=None, check_parse=True):
 
     def spans_score(gold_spans, system_spans):
         correct, gi, si = 0, 0, 0
-        undersegmented = list()
+        undersegmented = []
-        oversegmented = list()
+        oversegmented = []
         combo = 0
         previous_end_si_earlier = False
         previous_end_gi_earlier = False

bin/wiki_entity_linking/kb_creator.py

@@ -0,0 +1,171 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+from .train_descriptions import EntityEncoder
+from . import wikidata_processor as wd, wikipedia_processor as wp
+from spacy.kb import KnowledgeBase
+
+import csv
+import datetime
+
+
+INPUT_DIM = 300  # dimension of pre-trained input vectors
+DESC_WIDTH = 64  # dimension of output entity vectors
+
+
+def create_kb(nlp, max_entities_per_alias, min_entity_freq, min_occ,
+              entity_def_output, entity_descr_output,
+              count_input, prior_prob_input, wikidata_input):
+    # Create the knowledge base from Wikidata entries
+    kb = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=DESC_WIDTH)
+
+    # disable this part of the pipeline when rerunning the KB generation from preprocessed files
+    read_raw_data = True
+
+    if read_raw_data:
+        print()
+        print(" * _read_wikidata_entities", datetime.datetime.now())
+        title_to_id, id_to_descr = wd.read_wikidata_entities_json(wikidata_input)
+
+        # write the title-ID and ID-description mappings to file
+        _write_entity_files(entity_def_output, entity_descr_output, title_to_id, id_to_descr)
+
+    else:
+        # read the mappings from file
+        title_to_id = get_entity_to_id(entity_def_output)
+        id_to_descr = get_id_to_description(entity_descr_output)
+
+    print()
+    print(" * _get_entity_frequencies", datetime.datetime.now())
+    print()
+    entity_frequencies = wp.get_all_frequencies(count_input=count_input)
+
+    # filter the entities for in the KB by frequency, because there's just too much data (8M entities) otherwise
+    filtered_title_to_id = dict()
+    entity_list = []
+    description_list = []
+    frequency_list = []
+    for title, entity in title_to_id.items():
+        freq = entity_frequencies.get(title, 0)
+        desc = id_to_descr.get(entity, None)
+        if desc and freq > min_entity_freq:
+            entity_list.append(entity)
+            description_list.append(desc)
+            frequency_list.append(freq)
+            filtered_title_to_id[title] = entity
+
+    print("Kept", len(filtered_title_to_id.keys()), "out of", len(title_to_id.keys()),
+          "titles with filter frequency", min_entity_freq)
+
+    print()
+    print(" * train entity encoder", datetime.datetime.now())
+    print()
+    encoder = EntityEncoder(nlp, INPUT_DIM, DESC_WIDTH)
+    encoder.train(description_list=description_list, to_print=True)
+
+    print()
+    print(" * get entity embeddings", datetime.datetime.now())
+    print()
+    embeddings = encoder.apply_encoder(description_list)
+
+    print()
+    print(" * adding", len(entity_list), "entities", datetime.datetime.now())
+    kb.set_entities(entity_list=entity_list, prob_list=frequency_list, vector_list=embeddings)
+
+    print()
+    print(" * adding aliases", datetime.datetime.now())
+    print()
+    _add_aliases(kb, title_to_id=filtered_title_to_id,
+                 max_entities_per_alias=max_entities_per_alias, min_occ=min_occ,
+                 prior_prob_input=prior_prob_input)
+
+    print()
+    print("kb size:", len(kb), kb.get_size_entities(), kb.get_size_aliases())
+
+    print("done with kb", datetime.datetime.now())
+    return kb
+
+
+def _write_entity_files(entity_def_output, entity_descr_output, title_to_id, id_to_descr):
+    with open(entity_def_output, mode='w', encoding='utf8') as id_file:
+        id_file.write("WP_title" + "|" + "WD_id" + "\n")
+        for title, qid in title_to_id.items():
+            id_file.write(title + "|" + str(qid) + "\n")
+
+    with open(entity_descr_output, mode='w', encoding='utf8') as descr_file:
+        descr_file.write("WD_id" + "|" + "description" + "\n")
+        for qid, descr in id_to_descr.items():
+            descr_file.write(str(qid) + "|" + descr + "\n")
+
+
+def get_entity_to_id(entity_def_output):
+    entity_to_id = dict()
+    with open(entity_def_output, 'r', encoding='utf8') as csvfile:
+        csvreader = csv.reader(csvfile, delimiter='|')
+        # skip header
+        next(csvreader)
+        for row in csvreader:
+            entity_to_id[row[0]] = row[1]
+    return entity_to_id
+
+
+def get_id_to_description(entity_descr_output):
+    id_to_desc = dict()
+    with open(entity_descr_output, 'r', encoding='utf8') as csvfile:
+        csvreader = csv.reader(csvfile, delimiter='|')
+        # skip header
+        next(csvreader)
+        for row in csvreader:
+            id_to_desc[row[0]] = row[1]
+    return id_to_desc
+
+
+def _add_aliases(kb, title_to_id, max_entities_per_alias, min_occ, prior_prob_input):
+    wp_titles = title_to_id.keys()
+
+    # adding aliases with prior probabilities
+    # we can read this file sequentially, it's sorted by alias, and then by count
+    with open(prior_prob_input, mode='r', encoding='utf8') as prior_file:
+        # skip header
+        prior_file.readline()
+        line = prior_file.readline()
+        previous_alias = None
+        total_count = 0
+        counts = []
+        entities = []
+        while line:
+            splits = line.replace('\n', "").split(sep='|')
+            new_alias = splits[0]
+            count = int(splits[1])
+            entity = splits[2]
+
+            if new_alias != previous_alias and previous_alias:
+                # done reading the previous alias --> output
+                if len(entities) > 0:
+                    selected_entities = []
+                    prior_probs = []
+                    for ent_count, ent_string in zip(counts, entities):
+                        if ent_string in wp_titles:
+                            wd_id = title_to_id[ent_string]
+                            p_entity_givenalias = ent_count / total_count
+                            selected_entities.append(wd_id)
+                            prior_probs.append(p_entity_givenalias)
+
+                    if selected_entities:
+                        try:
+                            kb.add_alias(alias=previous_alias, entities=selected_entities, probabilities=prior_probs)
+                        except ValueError as e:
+                            print(e)
+                total_count = 0
+                counts = []
+                entities = []
+
+            total_count += count
+
+            if len(entities) < max_entities_per_alias and count >= min_occ:
+                counts.append(count)
+                entities.append(entity)
+            previous_alias = new_alias
+
+            line = prior_file.readline()
+

bin/wiki_entity_linking/train_descriptions.py

@@ -0,0 +1,121 @@
+# coding: utf-8
+from random import shuffle
+
+import numpy as np
+
+from spacy._ml import zero_init, create_default_optimizer
+from spacy.cli.pretrain import get_cossim_loss
+
+from thinc.v2v import Model
+from thinc.api import chain
+from thinc.neural._classes.affine import Affine
+
+
+class EntityEncoder:
+    """
+    Train the embeddings of entity descriptions to fit a fixed-size entity vector (e.g. 64D).
+    This entity vector will be stored in the KB, for further downstream use in the entity model.
+    """
+
+    DROP = 0
+    EPOCHS = 5
+    STOP_THRESHOLD = 0.04
+
+    BATCH_SIZE = 1000
+
+    def __init__(self, nlp, input_dim, desc_width):
+        self.nlp = nlp
+        self.input_dim = input_dim
+        self.desc_width = desc_width
+
+    def apply_encoder(self, description_list):
+        if self.encoder is None:
+            raise ValueError("Can not apply encoder before training it")
+
+        batch_size = 100000
+
+        start = 0
+        stop = min(batch_size, len(description_list))
+        encodings = []
+
+        while start < len(description_list):
+            docs = list(self.nlp.pipe(description_list[start:stop]))
+            doc_embeddings = [self._get_doc_embedding(doc) for doc in docs]
+            enc = self.encoder(np.asarray(doc_embeddings))
+            encodings.extend(enc.tolist())
+
+            start = start + batch_size
+            stop = min(stop + batch_size, len(description_list))
+
+        return encodings
+
+    def train(self, description_list, to_print=False):
+        processed, loss = self._train_model(description_list)
+        if to_print:
+            print("Trained on", processed, "entities across", self.EPOCHS, "epochs")
+            print("Final loss:", loss)
+
+    def _train_model(self, description_list):
+        # TODO: when loss gets too low, a 'mean of empty slice' warning is thrown by numpy
+
+        self._build_network(self.input_dim, self.desc_width)
+
+        processed = 0
+        loss = 1
+        descriptions = description_list.copy()   # copy this list so that shuffling does not affect other functions
+
+        for i in range(self.EPOCHS):
+            shuffle(descriptions)
+
+            batch_nr = 0
+            start = 0
+            stop = min(self.BATCH_SIZE, len(descriptions))
+
+            while loss > self.STOP_THRESHOLD and start < len(descriptions):
+                batch = []
+                for descr in descriptions[start:stop]:
+                    doc = self.nlp(descr)
+                    doc_vector = self._get_doc_embedding(doc)
+                    batch.append(doc_vector)
+
+                loss = self._update(batch)
+                print(i, batch_nr, loss)
+                processed += len(batch)
+
+                batch_nr += 1
+                start = start + self.BATCH_SIZE
+                stop = min(stop + self.BATCH_SIZE, len(descriptions))
+
+        return processed, loss
+
+    @staticmethod
+    def _get_doc_embedding(doc):
+        indices = np.zeros((len(doc),), dtype="i")
+        for i, word in enumerate(doc):
+            if word.orth in doc.vocab.vectors.key2row:
+                indices[i] = doc.vocab.vectors.key2row[word.orth]
+            else:
+                indices[i] = 0
+        word_vectors = doc.vocab.vectors.data[indices]
+        doc_vector = np.mean(word_vectors, axis=0)
+        return doc_vector
+
+    def _build_network(self, orig_width, hidden_with):
+        with Model.define_operators({">>": chain}):
+            # very simple encoder-decoder model
+            self.encoder = (
+                Affine(hidden_with, orig_width)
+            )
+            self.model = self.encoder >> zero_init(Affine(orig_width, hidden_with, drop_factor=0.0))
+        self.sgd = create_default_optimizer(self.model.ops)
+
+    def _update(self, vectors):
+        predictions, bp_model = self.model.begin_update(np.asarray(vectors), drop=self.DROP)
+        loss, d_scores = self._get_loss(scores=predictions, golds=np.asarray(vectors))
+        bp_model(d_scores, sgd=self.sgd)
+        return loss / len(vectors)
+
+    @staticmethod
+    def _get_loss(golds, scores):
+        loss, gradients = get_cossim_loss(scores, golds)
+        return loss, gradients

bin/wiki_entity_linking/training_set_creator.py

@@ -0,0 +1,353 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import os
+import re
+import bz2
+import datetime
+
+from spacy.gold import GoldParse
+from bin.wiki_entity_linking import kb_creator
+
+"""
+Process Wikipedia interlinks to generate a training dataset for the EL algorithm.
+Gold-standard entities are stored in one file in standoff format (by character offset).
+"""
+
+ENTITY_FILE = "gold_entities.csv"
+
+
+def create_training(wikipedia_input, entity_def_input, training_output):
+    wp_to_id = kb_creator.get_entity_to_id(entity_def_input)
+    _process_wikipedia_texts(wikipedia_input, wp_to_id, training_output, limit=None)
+
+
+def _process_wikipedia_texts(wikipedia_input, wp_to_id, training_output, limit=None):
+    """
+    Read the XML wikipedia data to parse out training data:
+    raw text data + positive instances
+    """
+    title_regex = re.compile(r'(?<=<title>).*(?=</title>)')
+    id_regex = re.compile(r'(?<=<id>)\d*(?=</id>)')
+
+    read_ids = set()
+    entityfile_loc = training_output / ENTITY_FILE
+    with open(entityfile_loc, mode="w", encoding='utf8') as entityfile:
+        # write entity training header file
+        _write_training_entity(outputfile=entityfile,
+                               article_id="article_id",
+                               alias="alias",
+                               entity="WD_id",
+                               start="start",
+                               end="end")
+
+        with bz2.open(wikipedia_input, mode='rb') as file:
+            line = file.readline()
+            cnt = 0
+            article_text = ""
+            article_title = None
+            article_id = None
+            reading_text = False
+            reading_revision = False
+            while line and (not limit or cnt < limit):
+                if cnt % 1000000 == 0:
+                    print(datetime.datetime.now(), "processed", cnt, "lines of Wikipedia dump")
+                clean_line = line.strip().decode("utf-8")
+
+                if clean_line == "<revision>":
+                    reading_revision = True
+                elif clean_line == "</revision>":
+                    reading_revision = False
+
+                # Start reading new page
+                if clean_line == "<page>":
+                    article_text = ""
+                    article_title = None
+                    article_id = None
+
+                # finished reading this page
+                elif clean_line == "</page>":
+                    if article_id:
+                        try:
+                            _process_wp_text(wp_to_id, entityfile, article_id, article_title, article_text.strip(),
+                                             training_output)
+                        except Exception as e:
+                            print("Error processing article", article_id, article_title, e)
+                    else:
+                        print("Done processing a page, but couldn't find an article_id ?", article_title)
+                    article_text = ""
+                    article_title = None
+                    article_id = None
+                    reading_text = False
+                    reading_revision = False
+
+                # start reading text within a page
+                if "<text" in clean_line:
+                    reading_text = True
+
+                if reading_text:
+                    article_text += " " + clean_line
+
+                # stop reading text within a page (we assume a new page doesn't start on the same line)
+                if "</text" in clean_line:
+                    reading_text = False
+
+                # read the ID of this article (outside the revision portion of the document)
+                if not reading_revision:
+                    ids = id_regex.search(clean_line)
+                    if ids:
+                        article_id = ids[0]
+                        if article_id in read_ids:
+                            print("Found duplicate article ID", article_id, clean_line)  # This should never happen ...
+                        read_ids.add(article_id)
+
+                # read the title of this article (outside the revision portion of the document)
+                if not reading_revision:
+                    titles = title_regex.search(clean_line)
+                    if titles:
+                        article_title = titles[0].strip()
+
+                line = file.readline()
+                cnt += 1
+
+
+text_regex = re.compile(r'(?<=<text xml:space=\"preserve\">).*(?=</text)')
+
+
+def _process_wp_text(wp_to_id, entityfile, article_id, article_title, article_text, training_output):
+    found_entities = False
+
+    # ignore meta Wikipedia pages
+    if article_title.startswith("Wikipedia:"):
+        return
+
+    # remove the text tags
+    text = text_regex.search(article_text).group(0)
+
+    # stop processing if this is a redirect page
+    if text.startswith("#REDIRECT"):
+        return
+
+    # get the raw text without markup etc, keeping only interwiki links
+    clean_text = _get_clean_wp_text(text)
+
+    # read the text char by char to get the right offsets for the interwiki links
+    final_text = ""
+    open_read = 0
+    reading_text = True
+    reading_entity = False
+    reading_mention = False
+    reading_special_case = False
+    entity_buffer = ""
+    mention_buffer = ""
+    for index, letter in enumerate(clean_text):
+        if letter == '[':
+            open_read += 1
+        elif letter == ']':
+            open_read -= 1
+        elif letter == '|':
+            if reading_text:
+                final_text += letter
+            # switch from reading entity to mention in the [[entity|mention]] pattern
+            elif reading_entity:
+                reading_text = False
+                reading_entity = False
+                reading_mention = True
+            else:
+                reading_special_case = True
+        else:
+            if reading_entity:
+                entity_buffer += letter
+            elif reading_mention:
+                mention_buffer += letter
+            elif reading_text:
+                final_text += letter
+            else:
+                raise ValueError("Not sure at point", clean_text[index-2:index+2])
+
+        if open_read > 2:
+            reading_special_case = True
+
+        if open_read == 2 and reading_text:
+            reading_text = False
+            reading_entity = True
+            reading_mention = False
+
+        # we just finished reading an entity
+        if open_read == 0 and not reading_text:
+            if '#' in entity_buffer or entity_buffer.startswith(':'):
+                reading_special_case = True
+            # Ignore cases with nested structures like File: handles etc
+            if not reading_special_case:
+                if not mention_buffer:
+                    mention_buffer = entity_buffer
+                start = len(final_text)
+                end = start + len(mention_buffer)
+                qid = wp_to_id.get(entity_buffer, None)
+                if qid:
+                    _write_training_entity(outputfile=entityfile,
+                                           article_id=article_id,
+                                           alias=mention_buffer,
+                                           entity=qid,
+                                           start=start,
+                                           end=end)
+                found_entities = True
+                final_text += mention_buffer
+
+            entity_buffer = ""
+            mention_buffer = ""
+
+            reading_text = True
+            reading_entity = False
+            reading_mention = False
+            reading_special_case = False
+
+    if found_entities:
+        _write_training_article(article_id=article_id, clean_text=final_text, training_output=training_output)
+
+
+info_regex = re.compile(r'{[^{]*?}')
+htlm_regex = re.compile(r'&lt;!--[^-]*--&gt;')
+category_regex = re.compile(r'\[\[Category:[^\[]*]]')
+file_regex = re.compile(r'\[\[File:[^[\]]+]]')
+ref_regex = re.compile(r'&lt;ref.*?&gt;')     # non-greedy
+ref_2_regex = re.compile(r'&lt;/ref.*?&gt;')  # non-greedy
+
+
+def _get_clean_wp_text(article_text):
+    clean_text = article_text.strip()
+
+    # remove bolding & italic markup
+    clean_text = clean_text.replace('\'\'\'', '')
+    clean_text = clean_text.replace('\'\'', '')
+
+    # remove nested {{info}} statements by removing the inner/smallest ones first and iterating
+    try_again = True
+    previous_length = len(clean_text)
+    while try_again:
+        clean_text = info_regex.sub('', clean_text)  # non-greedy match excluding a nested {
+        if len(clean_text) < previous_length:
+            try_again = True
+        else:
+            try_again = False
+        previous_length = len(clean_text)
+
+    # remove HTML comments
+    clean_text = htlm_regex.sub('', clean_text)
+
+    # remove Category and File statements
+    clean_text = category_regex.sub('', clean_text)
+    clean_text = file_regex.sub('', clean_text)
+
+    # remove multiple =
+    while '==' in clean_text:
+        clean_text = clean_text.replace("==", "=")
+
+    clean_text = clean_text.replace(". =", ".")
+    clean_text = clean_text.replace(" = ", ". ")
+    clean_text = clean_text.replace("= ", ".")
+    clean_text = clean_text.replace(" =", "")
+
+    # remove refs (non-greedy match)
+    clean_text = ref_regex.sub('', clean_text)
+    clean_text = ref_2_regex.sub('', clean_text)
+
+    # remove additional wikiformatting
+    clean_text = re.sub(r'&lt;blockquote&gt;', '', clean_text)
+    clean_text = re.sub(r'&lt;/blockquote&gt;', '', clean_text)
+
+    # change special characters back to normal ones
+    clean_text = clean_text.replace(r'&lt;', '<')
+    clean_text = clean_text.replace(r'&gt;', '>')
+    clean_text = clean_text.replace(r'&quot;', '"')
+    clean_text = clean_text.replace(r'&amp;nbsp;', ' ')
+    clean_text = clean_text.replace(r'&amp;', '&')
+
+    # remove multiple spaces
+    while '  ' in clean_text:
+        clean_text = clean_text.replace('  ', ' ')
+
+    return clean_text.strip()
+
+
+def _write_training_article(article_id, clean_text, training_output):
+    file_loc = training_output / str(article_id) + ".txt"
+    with open(file_loc, mode='w', encoding='utf8') as outputfile:
+        outputfile.write(clean_text)
+
+
+def _write_training_entity(outputfile, article_id, alias, entity, start, end):
+    outputfile.write(article_id + "|" + alias + "|" + entity + "|" + str(start) + "|" + str(end) + "\n")
+
+
+def is_dev(article_id):
+    return article_id.endswith("3")
+
+
+def read_training(nlp, training_dir, dev, limit):
+    # This method provides training examples that correspond to the entity annotations found by the nlp object
+    entityfile_loc = training_dir / ENTITY_FILE
+    data = []
+
+    # assume the data is written sequentially, so we can reuse the article docs
+    current_article_id = None
+    current_doc = None
+    ents_by_offset = dict()
+    skip_articles = set()
+    total_entities = 0
+
+    with open(entityfile_loc, mode='r', encoding='utf8') as file:
+        for line in file:
+            if not limit or len(data) < limit:
+                fields = line.replace('\n', "").split(sep='|')
+                article_id = fields[0]
+                alias = fields[1]
+                wp_title = fields[2]
+                start = fields[3]
+                end = fields[4]
+
+                if dev == is_dev(article_id) and article_id != "article_id" and article_id not in skip_articles:
+                    if not current_doc or (current_article_id != article_id):
+                        # parse the new article text
+                        file_name = article_id + ".txt"
+                        try:
+                            with open(os.path.join(training_dir, file_name), mode="r", encoding='utf8') as f:
+                                text = f.read()
+                                if len(text) < 30000:   # threshold for convenience / speed of processing
+                                    current_doc = nlp(text)
+                                    current_article_id = article_id
+                                    ents_by_offset = dict()
+                                    for ent in current_doc.ents:
+                                        sent_length = len(ent.sent)
+                                        # custom filtering to avoid too long or too short sentences
+                                        if 5 < sent_length < 100:
+                                            ents_by_offset[str(ent.start_char) + "_" + str(ent.end_char)] = ent
+                                else:
+                                    skip_articles.add(article_id)
+                                    current_doc = None
+                        except Exception as e:
+                            print("Problem parsing article", article_id, e)
+                            skip_articles.add(article_id)
+                            raise e
+
+                    # repeat checking this condition in case an exception was thrown
+                    if current_doc and (current_article_id == article_id):
+                        found_ent = ents_by_offset.get(start + "_" + end,  None)
+                        if found_ent:
+                            if found_ent.text != alias:
+                                skip_articles.add(article_id)
+                                current_doc = None
+                            else:
+                                sent = found_ent.sent.as_doc()
+                                # currently feeding the gold data one entity per sentence at a time
+                                gold_start = int(start) - found_ent.sent.start_char
+                                gold_end = int(end) - found_ent.sent.start_char
+                                gold_entities = [(gold_start, gold_end, wp_title)]
+                                gold = GoldParse(doc=sent, links=gold_entities)
+                                data.append((sent, gold))
+                                total_entities += 1
+                                if len(data) % 2500 == 0:
+                                    print(" -read", total_entities, "entities")
+
+    print(" -read", total_entities, "entities")
+    return data

bin/wiki_entity_linking/wikidata_processor.py

@@ -0,0 +1,119 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import bz2
+import json
+import datetime
+
+
+def read_wikidata_entities_json(wikidata_file, limit=None, to_print=False):
+    # Read the JSON wiki data and parse out the entities. Takes about 7u30 to parse 55M lines.
+    # get latest-all.json.bz2 from https://dumps.wikimedia.org/wikidatawiki/entities/
+
+    lang = 'en'
+    site_filter = 'enwiki'
+
+    # properties filter (currently disabled to get ALL data)
+    prop_filter = dict()
+    # prop_filter = {'P31': {'Q5', 'Q15632617'}}     # currently defined as OR: one property suffices to be selected
+
+    title_to_id = dict()
+    id_to_descr = dict()
+
+    # parse appropriate fields - depending on what we need in the KB
+    parse_properties = False
+    parse_sitelinks = True
+    parse_labels = False
+    parse_descriptions = True
+    parse_aliases = False
+    parse_claims = False
+
+    with bz2.open(wikidata_file, mode='rb') as file:
+        line = file.readline()
+        cnt = 0
+        while line and (not limit or cnt < limit):
+            if cnt % 500000 == 0:
+                print(datetime.datetime.now(), "processed", cnt, "lines of WikiData dump")
+            clean_line = line.strip()
+            if clean_line.endswith(b","):
+                clean_line = clean_line[:-1]
+            if len(clean_line) > 1:
+                obj = json.loads(clean_line)
+                entry_type = obj["type"]
+
+                if entry_type == "item":
+                    # filtering records on their properties (currently disabled to get ALL data)
+                    # keep = False
+                    keep = True
+
+                    claims = obj["claims"]
+                    if parse_claims:
+                        for prop, value_set in prop_filter.items():
+                            claim_property = claims.get(prop, None)
+                            if claim_property:
+                                for cp in claim_property:
+                                    cp_id = cp['mainsnak'].get('datavalue', {}).get('value', {}).get('id')
+                                    cp_rank = cp['rank']
+                                    if cp_rank != "deprecated" and cp_id in value_set:
+                                        keep = True
+
+                    if keep:
+                        unique_id = obj["id"]
+
+                        if to_print:
+                            print("ID:", unique_id)
+                            print("type:", entry_type)
+
+                        # parsing all properties that refer to other entities
+                        if parse_properties:
+                            for prop, claim_property in claims.items():
+                                cp_dicts = [cp['mainsnak']['datavalue'].get('value') for cp in claim_property
+                                            if cp['mainsnak'].get('datavalue')]
+                                cp_values = [cp_dict.get('id') for cp_dict in cp_dicts if isinstance(cp_dict, dict)
+                                             if cp_dict.get('id') is not None]
+                                if cp_values:
+                                    if to_print:
+                                        print("prop:", prop, cp_values)
+
+                        found_link = False
+                        if parse_sitelinks:
+                            site_value = obj["sitelinks"].get(site_filter, None)
+                            if site_value:
+                                site = site_value['title']
+                                if to_print:
+                                    print(site_filter, ":", site)
+                                title_to_id[site] = unique_id
+                                found_link = True
+
+                        if parse_labels:
+                            labels = obj["labels"]
+                            if labels:
+                                lang_label = labels.get(lang, None)
+                                if lang_label:
+                                    if to_print:
+                                        print("label (" + lang + "):", lang_label["value"])
+
+                        if found_link and parse_descriptions:
+                            descriptions = obj["descriptions"]
+                            if descriptions:
+                                lang_descr = descriptions.get(lang, None)
+                                if lang_descr:
+                                    if to_print:
+                                        print("description (" + lang + "):", lang_descr["value"])
+                                    id_to_descr[unique_id] = lang_descr["value"]
+
+                        if parse_aliases:
+                            aliases = obj["aliases"]
+                            if aliases:
+                                lang_aliases = aliases.get(lang, None)
+                                if lang_aliases:
+                                    for item in lang_aliases:
+                                        if to_print:
+                                            print("alias (" + lang + "):", item["value"])
+
+                        if to_print:
+                            print()
+            line = file.readline()
+            cnt += 1
+
+    return title_to_id, id_to_descr

bin/wiki_entity_linking/wikipedia_processor.py

@@ -0,0 +1,182 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import re
+import bz2
+import csv
+import datetime
+
+"""
+Process a Wikipedia dump to calculate entity frequencies and prior probabilities in combination with certain mentions.
+Write these results to file for downstream KB and training data generation.
+"""
+
+map_alias_to_link = dict()
+
+# these will/should be matched ignoring case
+wiki_namespaces = ["b", "betawikiversity", "Book", "c", "Category", "Commons",
+                   "d", "dbdump", "download", "Draft", "Education", "Foundation",
+                   "Gadget", "Gadget definition", "gerrit", "File", "Help", "Image", "Incubator",
+                   "m", "mail", "mailarchive", "media", "MediaWiki", "MediaWiki talk", "Mediawikiwiki",
+                   "MediaZilla", "Meta", "Metawikipedia", "Module",
+                   "mw", "n", "nost", "oldwikisource", "outreach", "outreachwiki", "otrs", "OTRSwiki",
+                   "Portal", "phab", "Phabricator", "Project", "q", "quality", "rev",
+                   "s", "spcom", "Special", "species", "Strategy", "sulutil", "svn",
+                   "Talk", "Template", "Template talk", "Testwiki", "ticket", "TimedText", "Toollabs", "tools",
+                   "tswiki", "User", "User talk", "v", "voy",
+                   "w", "Wikibooks", "Wikidata", "wikiHow", "Wikinvest", "wikilivres", "Wikimedia", "Wikinews",
+                   "Wikipedia", "Wikipedia talk", "Wikiquote", "Wikisource", "Wikispecies", "Wikitech",
+                   "Wikiversity", "Wikivoyage", "wikt", "wiktionary", "wmf", "wmania", "WP"]
+
+# find the links
+link_regex = re.compile(r'\[\[[^\[\]]*\]\]')
+
+# match on interwiki links, e.g. `en:` or `:fr:`
+ns_regex = r":?" + "[a-z][a-z]" + ":"
+
+# match on Namespace: optionally preceded by a :
+for ns in wiki_namespaces:
+    ns_regex += "|" + ":?" + ns + ":"
+
+ns_regex = re.compile(ns_regex, re.IGNORECASE)
+
+
+def read_wikipedia_prior_probs(wikipedia_input, prior_prob_output):
+    """
+    Read the XML wikipedia data and parse out intra-wiki links to estimate prior probabilities.
+    The full file takes about 2h to parse 1100M lines.
+    It works relatively fast because it runs line by line, irrelevant of which article the intrawiki is from.
+    """
+    with bz2.open(wikipedia_input, mode='rb') as file:
+        line = file.readline()
+        cnt = 0
+        while line:
+            if cnt % 5000000 == 0:
+                print(datetime.datetime.now(), "processed", cnt, "lines of Wikipedia dump")
+            clean_line = line.strip().decode("utf-8")
+
+            aliases, entities, normalizations = get_wp_links(clean_line)
+            for alias, entity, norm in zip(aliases, entities, normalizations):
+                _store_alias(alias, entity, normalize_alias=norm, normalize_entity=True)
+                _store_alias(alias, entity, normalize_alias=norm, normalize_entity=True)
+
+            line = file.readline()
+            cnt += 1
+
+    # write all aliases and their entities and count occurrences to file
+    with open(prior_prob_output, mode='w', encoding='utf8') as outputfile:
+        outputfile.write("alias" + "|" + "count" + "|" + "entity" + "\n")
+        for alias, alias_dict in sorted(map_alias_to_link.items(), key=lambda x: x[0]):
+            for entity, count in sorted(alias_dict.items(), key=lambda x: x[1], reverse=True):
+                outputfile.write(alias + "|" + str(count) + "|" + entity + "\n")
+
+
+def _store_alias(alias, entity, normalize_alias=False, normalize_entity=True):
+    alias = alias.strip()
+    entity = entity.strip()
+
+    # remove everything after # as this is not part of the title but refers to a specific paragraph
+    if normalize_entity:
+        # wikipedia titles are always capitalized
+        entity = _capitalize_first(entity.split("#")[0])
+    if normalize_alias:
+        alias = alias.split("#")[0]
+
+    if alias and entity:
+        alias_dict = map_alias_to_link.get(alias, dict())
+        entity_count = alias_dict.get(entity, 0)
+        alias_dict[entity] = entity_count + 1
+        map_alias_to_link[alias] = alias_dict
+
+
+def get_wp_links(text):
+    aliases = []
+    entities = []
+    normalizations = []
+
+    matches = link_regex.findall(text)
+    for match in matches:
+        match = match[2:][:-2].replace("_", " ").strip()
+
+        if ns_regex.match(match):
+            pass  # ignore namespaces at the beginning of the string
+
+        # this is a simple [[link]], with the alias the same as the mention
+        elif "|" not in match:
+            aliases.append(match)
+            entities.append(match)
+            normalizations.append(True)
+
+        # in wiki format, the link is written as [[entity|alias]]
+        else:
+            splits = match.split("|")
+            entity = splits[0].strip()
+            alias = splits[1].strip()
+            # specific wiki format  [[alias (specification)|]]
+            if len(alias) == 0 and "(" in entity:
+                alias = entity.split("(")[0]
+                aliases.append(alias)
+                entities.append(entity)
+                normalizations.append(False)
+            else:
+                aliases.append(alias)
+                entities.append(entity)
+                normalizations.append(False)
+
+    return aliases, entities, normalizations
+
+
+def _capitalize_first(text):
+    if not text:
+        return None
+    result = text[0].capitalize()
+    if len(result) > 0:
+        result += text[1:]
+    return result
+
+
+def write_entity_counts(prior_prob_input, count_output, to_print=False):
+    # Write entity counts for quick access later
+    entity_to_count = dict()
+    total_count = 0
+
+    with open(prior_prob_input, mode='r', encoding='utf8') as prior_file:
+        # skip header
+        prior_file.readline()
+        line = prior_file.readline()
+
+        while line:
+            splits = line.replace('\n', "").split(sep='|')
+            # alias = splits[0]
+            count = int(splits[1])
+            entity = splits[2]
+
+            current_count = entity_to_count.get(entity, 0)
+            entity_to_count[entity] = current_count + count
+
+            total_count += count
+
+            line = prior_file.readline()
+
+    with open(count_output, mode='w', encoding='utf8') as entity_file:
+        entity_file.write("entity" + "|" + "count" + "\n")
+        for entity, count in entity_to_count.items():
+            entity_file.write(entity + "|" + str(count) + "\n")
+
+    if to_print:
+        for entity, count in entity_to_count.items():
+            print("Entity count:", entity, count)
+        print("Total count:", total_count)
+
+
+def get_all_frequencies(count_input):
+    entity_to_count = dict()
+    with open(count_input, 'r', encoding='utf8') as csvfile:
+        csvreader = csv.reader(csvfile, delimiter='|')
+        # skip header
+        next(csvreader)
+        for row in csvreader:
+            entity_to_count[row[0]] = int(row[1])
+
+    return entity_to_count
+

examples/pipeline/dummy_entity_linking.py

@@ -9,26 +9,26 @@ from spacy.kb import KnowledgeBase
 
 
 def create_kb(vocab):
-    kb = KnowledgeBase(vocab=vocab)
+    kb = KnowledgeBase(vocab=vocab, entity_vector_length=1)
 
     # adding entities
     entity_0 = "Q1004791_Douglas"
     print("adding entity", entity_0)
-    kb.add_entity(entity=entity_0, prob=0.5)
+    kb.add_entity(entity=entity_0, prob=0.5, entity_vector=[0])
 
     entity_1 = "Q42_Douglas_Adams"
     print("adding entity", entity_1)
-    kb.add_entity(entity=entity_1, prob=0.5)
+    kb.add_entity(entity=entity_1, prob=0.5, entity_vector=[1])
 
     entity_2 = "Q5301561_Douglas_Haig"
     print("adding entity", entity_2)
-    kb.add_entity(entity=entity_2, prob=0.5)
+    kb.add_entity(entity=entity_2, prob=0.5, entity_vector=[2])
 
     # adding aliases
     print()
     alias_0 = "Douglas"
     print("adding alias", alias_0)
-    kb.add_alias(alias=alias_0, entities=[entity_0, entity_1, entity_2], probabilities=[0.1, 0.6, 0.2])
+    kb.add_alias(alias=alias_0, entities=[entity_0, entity_1, entity_2], probabilities=[0.6, 0.1, 0.2])
 
     alias_1 = "Douglas Adams"
     print("adding alias", alias_1)
@@ -41,8 +41,12 @@ def create_kb(vocab):
 
 
 def add_el(kb, nlp):
-    el_pipe = nlp.create_pipe(name='entity_linker', config={"kb": kb})
+    el_pipe = nlp.create_pipe(name='entity_linker', config={"context_width": 64})
+    el_pipe.set_kb(kb)
     nlp.add_pipe(el_pipe, last=True)
+    nlp.begin_training()
+    el_pipe.context_weight = 0
+    el_pipe.prior_weight = 1
 
     for alias in ["Douglas Adams", "Douglas"]:
         candidates = nlp.linker.kb.get_candidates(alias)
@@ -66,6 +70,6 @@ def add_el(kb, nlp):
 
 
 if __name__ == "__main__":
-    nlp = spacy.load('en_core_web_sm')
+    my_nlp = spacy.load('en_core_web_sm')
-    my_kb = create_kb(nlp.vocab)
+    my_kb = create_kb(my_nlp.vocab)
-    add_el(my_kb, nlp)
+    add_el(my_kb, my_nlp)

examples/pipeline/wikidata_entity_linking.py

@@ -0,0 +1,442 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import random
+import datetime
+from pathlib import Path
+
+from bin.wiki_entity_linking import training_set_creator, kb_creator, wikipedia_processor as wp
+from bin.wiki_entity_linking.kb_creator import DESC_WIDTH
+
+import spacy
+from spacy.kb import KnowledgeBase
+from spacy.util import minibatch, compounding
+
+"""
+Demonstrate how to build a knowledge base from WikiData and run an Entity Linking algorithm.
+"""
+
+ROOT_DIR = Path("C:/Users/Sofie/Documents/data/")
+OUTPUT_DIR = ROOT_DIR / 'wikipedia'
+TRAINING_DIR = OUTPUT_DIR / 'training_data_nel'
+
+PRIOR_PROB = OUTPUT_DIR / 'prior_prob.csv'
+ENTITY_COUNTS = OUTPUT_DIR / 'entity_freq.csv'
+ENTITY_DEFS = OUTPUT_DIR / 'entity_defs.csv'
+ENTITY_DESCR = OUTPUT_DIR / 'entity_descriptions.csv'
+
+KB_FILE = OUTPUT_DIR / 'kb_1' / 'kb'
+NLP_1_DIR = OUTPUT_DIR / 'nlp_1'
+NLP_2_DIR = OUTPUT_DIR / 'nlp_2'
+
+# get latest-all.json.bz2 from https://dumps.wikimedia.org/wikidatawiki/entities/
+WIKIDATA_JSON = ROOT_DIR / 'wikidata' / 'wikidata-20190304-all.json.bz2'
+
+# get enwiki-latest-pages-articles-multistream.xml.bz2 from https://dumps.wikimedia.org/enwiki/latest/
+ENWIKI_DUMP = ROOT_DIR / 'wikipedia' / 'enwiki-20190320-pages-articles-multistream.xml.bz2'
+
+# KB construction parameters
+MAX_CANDIDATES = 10
+MIN_ENTITY_FREQ = 20
+MIN_PAIR_OCC = 5
+
+# model training parameters
+EPOCHS = 10
+DROPOUT = 0.5
+LEARN_RATE = 0.005
+L2 = 1e-6
+CONTEXT_WIDTH = 128
+
+
+def run_pipeline():
+    # set the appropriate booleans to define which parts of the pipeline should be re(run)
+    print("START", datetime.datetime.now())
+    print()
+    nlp_1 = spacy.load('en_core_web_lg')
+    nlp_2 = None
+    kb_2 = None
+
+    # one-time methods to create KB and write to file
+    to_create_prior_probs = False
+    to_create_entity_counts = False
+    to_create_kb = False
+
+    # read KB back in from file
+    to_read_kb = True
+    to_test_kb = False
+
+    # create training dataset
+    create_wp_training = False
+
+    # train the EL pipe
+    train_pipe = True
+    measure_performance = True
+
+    # test the EL pipe on a simple example
+    to_test_pipeline = True
+
+    # write the NLP object, read back in and test again
+    to_write_nlp = True
+    to_read_nlp = True
+    test_from_file = False
+
+    # STEP 1 : create prior probabilities from WP (run only once)
+    if to_create_prior_probs:
+        print("STEP 1: to_create_prior_probs", datetime.datetime.now())
+        wp.read_wikipedia_prior_probs(wikipedia_input=ENWIKI_DUMP, prior_prob_output=PRIOR_PROB)
+        print()
+
+    # STEP 2 : deduce entity frequencies from WP (run only once)
+    if to_create_entity_counts:
+        print("STEP 2: to_create_entity_counts", datetime.datetime.now())
+        wp.write_entity_counts(prior_prob_input=PRIOR_PROB, count_output=ENTITY_COUNTS, to_print=False)
+        print()
+
+    # STEP 3 : create KB and write to file (run only once)
+    if to_create_kb:
+        print("STEP 3a: to_create_kb", datetime.datetime.now())
+        kb_1 = kb_creator.create_kb(nlp_1,
+                                    max_entities_per_alias=MAX_CANDIDATES,
+                                    min_entity_freq=MIN_ENTITY_FREQ,
+                                    min_occ=MIN_PAIR_OCC,
+                                    entity_def_output=ENTITY_DEFS,
+                                    entity_descr_output=ENTITY_DESCR,
+                                    count_input=ENTITY_COUNTS,
+                                    prior_prob_input=PRIOR_PROB,
+                                    wikidata_input=WIKIDATA_JSON)
+        print("kb entities:", kb_1.get_size_entities())
+        print("kb aliases:", kb_1.get_size_aliases())
+        print()
+
+        print("STEP 3b: write KB and NLP", datetime.datetime.now())
+        kb_1.dump(KB_FILE)
+        nlp_1.to_disk(NLP_1_DIR)
+        print()
+
+    # STEP 4 : read KB back in from file
+    if to_read_kb:
+        print("STEP 4: to_read_kb", datetime.datetime.now())
+        nlp_2 = spacy.load(NLP_1_DIR)
+        kb_2 = KnowledgeBase(vocab=nlp_2.vocab, entity_vector_length=DESC_WIDTH)
+        kb_2.load_bulk(KB_FILE)
+        print("kb entities:", kb_2.get_size_entities())
+        print("kb aliases:", kb_2.get_size_aliases())
+        print()
+
+        # test KB
+        if to_test_kb:
+            check_kb(kb_2)
+            print()
+
+    # STEP 5: create a training dataset from WP
+    if create_wp_training:
+        print("STEP 5: create training dataset", datetime.datetime.now())
+        training_set_creator.create_training(wikipedia_input=ENWIKI_DUMP,
+                                             entity_def_input=ENTITY_DEFS,
+                                             training_output=TRAINING_DIR)
+
+    # STEP 6: create and train the entity linking pipe
+    if train_pipe:
+        print("STEP 6: training Entity Linking pipe", datetime.datetime.now())
+        type_to_int = {label: i for i, label in enumerate(nlp_2.entity.labels)}
+        print(" -analysing", len(type_to_int), "different entity types")
+        el_pipe = nlp_2.create_pipe(name='entity_linker',
+                                    config={"context_width": CONTEXT_WIDTH,
+                                            "pretrained_vectors": nlp_2.vocab.vectors.name,
+                                            "type_to_int": type_to_int})
+        el_pipe.set_kb(kb_2)
+        nlp_2.add_pipe(el_pipe, last=True)
+
+        other_pipes = [pipe for pipe in nlp_2.pipe_names if pipe != "entity_linker"]
+        with nlp_2.disable_pipes(*other_pipes):  # only train Entity Linking
+            optimizer = nlp_2.begin_training()
+            optimizer.learn_rate = LEARN_RATE
+            optimizer.L2 = L2
+
+        # define the size (nr of entities) of training and dev set
+        train_limit = 5000
+        dev_limit = 5000
+
+        train_data = training_set_creator.read_training(nlp=nlp_2,
+                                                        training_dir=TRAINING_DIR,
+                                                        dev=False,
+                                                        limit=train_limit)
+
+        print("Training on", len(train_data), "articles")
+        print()
+
+        dev_data = training_set_creator.read_training(nlp=nlp_2,
+                                                      training_dir=TRAINING_DIR,
+                                                      dev=True,
+                                                      limit=dev_limit)
+
+        print("Dev testing on", len(dev_data), "articles")
+        print()
+
+        if not train_data:
+            print("Did not find any training data")
+        else:
+            for itn in range(EPOCHS):
+                random.shuffle(train_data)
+                losses = {}
+                batches = minibatch(train_data, size=compounding(4.0, 128.0, 1.001))
+                batchnr = 0
+
+                with nlp_2.disable_pipes(*other_pipes):
+                    for batch in batches:
+                        try:
+                            docs, golds = zip(*batch)
+                            nlp_2.update(
+                                docs,
+                                golds,
+                                sgd=optimizer,
+                                drop=DROPOUT,
+                                losses=losses,
+                            )
+                            batchnr += 1
+                        except Exception as e:
+                            print("Error updating batch:", e)
+
+                if batchnr > 0:
+                    el_pipe.cfg["context_weight"] = 1
+                    el_pipe.cfg["prior_weight"] = 1
+                    dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe)
+                    losses['entity_linker'] = losses['entity_linker'] / batchnr
+                    print("Epoch, train loss", itn, round(losses['entity_linker'], 2),
+                          " / dev acc avg", round(dev_acc_context, 3))
+
+        # STEP 7: measure the performance of our trained pipe on an independent dev set
+        if len(dev_data) and measure_performance:
+            print()
+            print("STEP 7: performance measurement of Entity Linking pipe", datetime.datetime.now())
+            print()
+
+            counts, acc_r, acc_r_label, acc_p, acc_p_label, acc_o, acc_o_label = _measure_baselines(dev_data, kb_2)
+            print("dev counts:", sorted(counts.items(), key=lambda x: x[0]))
+            print("dev acc oracle:", round(acc_o, 3), [(x, round(y, 3)) for x, y in acc_o_label.items()])
+            print("dev acc random:", round(acc_r, 3), [(x, round(y, 3)) for x, y in acc_r_label.items()])
+            print("dev acc prior:", round(acc_p, 3), [(x, round(y, 3)) for x, y in acc_p_label.items()])
+
+            # using only context
+            el_pipe.cfg["context_weight"] = 1
+            el_pipe.cfg["prior_weight"] = 0
+            dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe)
+            print("dev acc context avg:", round(dev_acc_context, 3),
+                  [(x, round(y, 3)) for x, y in dev_acc_context_dict.items()])
+
+            # measuring combined accuracy (prior + context)
+            el_pipe.cfg["context_weight"] = 1
+            el_pipe.cfg["prior_weight"] = 1
+            dev_acc_combo, dev_acc_combo_dict = _measure_accuracy(dev_data, el_pipe, error_analysis=False)
+            print("dev acc combo avg:", round(dev_acc_combo, 3),
+                  [(x, round(y, 3)) for x, y in dev_acc_combo_dict.items()])
+
+        # STEP 8: apply the EL pipe on a toy example
+        if to_test_pipeline:
+            print()
+            print("STEP 8: applying Entity Linking to toy example", datetime.datetime.now())
+            print()
+            run_el_toy_example(nlp=nlp_2)
+
+        # STEP 9: write the NLP pipeline (including entity linker) to file
+        if to_write_nlp:
+            print()
+            print("STEP 9: testing NLP IO", datetime.datetime.now())
+            print()
+            print("writing to", NLP_2_DIR)
+            nlp_2.to_disk(NLP_2_DIR)
+            print()
+
+    # verify that the IO has gone correctly
+    if to_read_nlp:
+        print("reading from", NLP_2_DIR)
+        nlp_3 = spacy.load(NLP_2_DIR)
+
+        print("running toy example with NLP 3")
+        run_el_toy_example(nlp=nlp_3)
+
+    # testing performance with an NLP model from file
+    if test_from_file:
+        nlp_2 = spacy.load(NLP_1_DIR)
+        nlp_3 = spacy.load(NLP_2_DIR)
+        el_pipe = nlp_3.get_pipe("entity_linker")
+
+        dev_limit = 5000
+        dev_data = training_set_creator.read_training(nlp=nlp_2,
+                                                      training_dir=TRAINING_DIR,
+                                                      dev=True,
+                                                      limit=dev_limit)
+
+        print("Dev testing from file on", len(dev_data), "articles")
+        print()
+
+        dev_acc_combo, dev_acc_combo_dict = _measure_accuracy(dev_data, el_pipe=el_pipe, error_analysis=False)
+        print("dev acc combo avg:", round(dev_acc_combo, 3),
+              [(x, round(y, 3)) for x, y in dev_acc_combo_dict.items()])
+
+    print()
+    print("STOP", datetime.datetime.now())
+
+
+def _measure_accuracy(data, el_pipe=None, error_analysis=False):
+    # If the docs in the data require further processing with an entity linker, set el_pipe
+    correct_by_label = dict()
+    incorrect_by_label = dict()
+
+    docs = [d for d, g in data if len(d) > 0]
+    if el_pipe is not None:
+        docs = list(el_pipe.pipe(docs))
+    golds = [g for d, g in data if len(d) > 0]
+
+    for doc, gold in zip(docs, golds):
+        try:
+            correct_entries_per_article = dict()
+            for entity in gold.links:
+                start, end, gold_kb = entity
+                correct_entries_per_article[str(start) + "-" + str(end)] = gold_kb
+
+            for ent in doc.ents:
+                ent_label = ent.label_
+                pred_entity = ent.kb_id_
+                start = ent.start_char
+                end = ent.end_char
+                gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), None)
+                # the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
+                if gold_entity is not None:
+                    if gold_entity == pred_entity:
+                        correct = correct_by_label.get(ent_label, 0)
+                        correct_by_label[ent_label] = correct + 1
+                    else:
+                        incorrect = incorrect_by_label.get(ent_label, 0)
+                        incorrect_by_label[ent_label] = incorrect + 1
+                        if error_analysis:
+                            print(ent.text, "in", doc)
+                            print("Predicted",  pred_entity, "should have been", gold_entity)
+                            print()
+
+        except Exception as e:
+            print("Error assessing accuracy", e)
+
+    acc, acc_by_label = calculate_acc(correct_by_label,  incorrect_by_label)
+    return acc, acc_by_label
+
+
+def _measure_baselines(data, kb):
+    # Measure 3 performance baselines: random selection, prior probabilities, and 'oracle' prediction for upper bound
+    counts_by_label = dict()
+
+    random_correct_by_label = dict()
+    random_incorrect_by_label = dict()
+
+    oracle_correct_by_label = dict()
+    oracle_incorrect_by_label = dict()
+
+    prior_correct_by_label = dict()
+    prior_incorrect_by_label = dict()
+
+    docs = [d for d, g in data if len(d) > 0]
+    golds = [g for d, g in data if len(d) > 0]
+
+    for doc, gold in zip(docs, golds):
+        try:
+            correct_entries_per_article = dict()
+            for entity in gold.links:
+                start, end, gold_kb = entity
+                correct_entries_per_article[str(start) + "-" + str(end)] = gold_kb
+
+            for ent in doc.ents:
+                ent_label = ent.label_
+                start = ent.start_char
+                end = ent.end_char
+                gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), None)
+
+                # the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
+                if gold_entity is not None:
+                    counts_by_label[ent_label] = counts_by_label.get(ent_label, 0) + 1
+                    candidates = kb.get_candidates(ent.text)
+                    oracle_candidate = ""
+                    best_candidate = ""
+                    random_candidate = ""
+                    if candidates:
+                        scores = []
+
+                        for c in candidates:
+                            scores.append(c.prior_prob)
+                            if c.entity_ == gold_entity:
+                                oracle_candidate = c.entity_
+
+                        best_index = scores.index(max(scores))
+                        best_candidate = candidates[best_index].entity_
+                        random_candidate = random.choice(candidates).entity_
+
+                    if gold_entity == best_candidate:
+                        prior_correct_by_label[ent_label] = prior_correct_by_label.get(ent_label, 0) + 1
+                    else:
+                        prior_incorrect_by_label[ent_label] = prior_incorrect_by_label.get(ent_label, 0) + 1
+
+                    if gold_entity == random_candidate:
+                        random_correct_by_label[ent_label] = random_correct_by_label.get(ent_label, 0) + 1
+                    else:
+                        random_incorrect_by_label[ent_label] = random_incorrect_by_label.get(ent_label, 0) + 1
+
+                    if gold_entity == oracle_candidate:
+                        oracle_correct_by_label[ent_label] = oracle_correct_by_label.get(ent_label, 0) + 1
+                    else:
+                        oracle_incorrect_by_label[ent_label] = oracle_incorrect_by_label.get(ent_label, 0) + 1
+
+        except Exception as e:
+            print("Error assessing accuracy", e)
+
+    acc_prior, acc_prior_by_label = calculate_acc(prior_correct_by_label, prior_incorrect_by_label)
+    acc_rand, acc_rand_by_label = calculate_acc(random_correct_by_label, random_incorrect_by_label)
+    acc_oracle, acc_oracle_by_label = calculate_acc(oracle_correct_by_label, oracle_incorrect_by_label)
+
+    return counts_by_label, acc_rand, acc_rand_by_label, acc_prior, acc_prior_by_label, acc_oracle, acc_oracle_by_label
+
+
+def calculate_acc(correct_by_label, incorrect_by_label):
+    acc_by_label = dict()
+    total_correct = 0
+    total_incorrect = 0
+    all_keys = set()
+    all_keys.update(correct_by_label.keys())
+    all_keys.update(incorrect_by_label.keys())
+    for label in sorted(all_keys):
+        correct = correct_by_label.get(label, 0)
+        incorrect = incorrect_by_label.get(label, 0)
+        total_correct += correct
+        total_incorrect += incorrect
+        if correct == incorrect == 0:
+            acc_by_label[label] = 0
+        else:
+            acc_by_label[label] = correct / (correct + incorrect)
+    acc = 0
+    if not (total_correct == total_incorrect == 0):
+        acc = total_correct / (total_correct + total_incorrect)
+    return acc, acc_by_label
+
+
+def check_kb(kb):
+    for mention in ("Bush", "Douglas Adams", "Homer", "Brazil", "China"):
+        candidates = kb.get_candidates(mention)
+
+        print("generating candidates for " + mention + " :")
+        for c in candidates:
+            print(" ", c.prior_prob, c.alias_, "-->", c.entity_ + " (freq=" + str(c.entity_freq) + ")")
+        print()
+
+
+def run_el_toy_example(nlp):
+    text = "In The Hitchhiker's Guide to the Galaxy, written by Douglas Adams, " \
+           "Douglas reminds us to always bring our towel, even in China or Brazil. " \
+           "The main character in Doug's novel is the man Arthur Dent, " \
+           "but Douglas doesn't write about George Washington or Homer Simpson."
+    doc = nlp(text)
+    print(text)
+    for ent in doc.ents:
+        print(" ent", ent.text, ent.label_, ent.kb_id_)
+    print()
+
+
+if __name__ == "__main__":
+    run_pipeline()

spacy/_ml.py

@@ -652,6 +652,38 @@ def build_simple_cnn_text_classifier(tok2vec, nr_class, exclusive_classes=False,
     return model
 
 
+def build_nel_encoder(embed_width, hidden_width, ner_types, **cfg):
+    # TODO proper error
+    if "entity_width" not in cfg:
+        raise ValueError("entity_width not found")
+    if "context_width" not in cfg:
+        raise ValueError("context_width not found")
+
+    conv_depth = cfg.get("conv_depth", 2)
+    cnn_maxout_pieces = cfg.get("cnn_maxout_pieces", 3)
+    pretrained_vectors = cfg.get("pretrained_vectors")   # self.nlp.vocab.vectors.name
+    context_width = cfg.get("context_width")
+    entity_width = cfg.get("entity_width")
+
+    with Model.define_operators({">>": chain, "**": clone}):
+        model = Affine(entity_width, entity_width+context_width+1+ner_types)\
+                >> Affine(1, entity_width, drop_factor=0.0)\
+                >> logistic
+
+        # context encoder
+        tok2vec = Tok2Vec(width=hidden_width, embed_size=embed_width, pretrained_vectors=pretrained_vectors,
+                          cnn_maxout_pieces=cnn_maxout_pieces, subword_features=True, conv_depth=conv_depth,
+                          bilstm_depth=0) >> flatten_add_lengths >> Pooling(mean_pool)\
+                                >> Residual(zero_init(Maxout(hidden_width, hidden_width))) \
+                                >> zero_init(Affine(context_width, hidden_width))
+
+        model.tok2vec = tok2vec
+
+    model.tok2vec = tok2vec
+    model.tok2vec.nO = context_width
+    model.nO = 1
+    return model
+
 @layerize
 def flatten(seqs, drop=0.0):
     ops = Model.ops

spacy/attrs.pxd

@@ -82,6 +82,7 @@ cdef enum attr_id_t:
     DEP
     ENT_IOB
     ENT_TYPE
+    ENT_KB_ID
     HEAD
     SENT_START
     SPACY

spacy/attrs.pyx

@@ -84,6 +84,7 @@ IDS = {
     "DEP": DEP,
     "ENT_IOB": ENT_IOB,
     "ENT_TYPE": ENT_TYPE,
+    "ENT_KB_ID": ENT_KB_ID,
     "HEAD": HEAD,
     "SENT_START": SENT_START,
     "SPACY": SPACY,

spacy/cli/pretrain.py

@@ -301,7 +301,7 @@ def get_vectors_loss(ops, docs, prediction, objective="L2"):
     elif objective == "cosine":
         loss, d_target = get_cossim_loss(prediction, target)
     else:
-        raise ValueError(Errors.E139.format(loss_func=objective))
+        raise ValueError(Errors.E142.format(loss_func=objective))
     return loss, d_target
 
 

spacy/errors.py

@@ -399,7 +399,10 @@ class Errors(object):
     E138 = ("Invalid JSONL format for raw text '{text}'. Make sure the input includes either the "
             "`text` or `tokens` key. For more info, see the docs:\n"
             "https://spacy.io/api/cli#pretrain-jsonl")
-    E139 = ("Unsupported loss_function '{loss_func}'. Use either 'L2' or 'cosine'")
+    E139 = ("Knowledge base for component '{name}' not initialized. Did you forget to call set_kb()?")
+    E140 = ("The list of entities, prior probabilities and entity vectors should be of equal length.")
+    E141 = ("Entity vectors should be of length {required} instead of the provided {found}.")
+    E142 = ("Unsupported loss_function '{loss_func}'. Use either 'L2' or 'cosine'")
 
 
 @add_codes

spacy/gold.pxd

@@ -31,6 +31,7 @@ cdef class GoldParse:
     cdef public list ents
     cdef public dict brackets
     cdef public object cats
+    cdef public list links
 
     cdef readonly list cand_to_gold
     cdef readonly list gold_to_cand

spacy/gold.pyx

@@ -427,7 +427,7 @@ cdef class GoldParse:
 
     def __init__(self, doc, annot_tuples=None, words=None, tags=None,
                  heads=None, deps=None, entities=None, make_projective=False,
-                 cats=None, **_):
+                 cats=None, links=None, **_):
         """Create a GoldParse.
 
         doc (Doc): The document the annotations refer to.
@@ -450,6 +450,8 @@ cdef class GoldParse:
             examples of a label to have the value 0.0. Labels not in the
             dictionary are treated as missing - the gradient for those labels
             will be zero.
+        links (iterable): A sequence of `(start_char, end_char, kb_id)` tuples,
+            representing the external ID of an entity in a knowledge base.
         RETURNS (GoldParse): The newly constructed object.
         """
         if words is None:
@@ -485,6 +487,7 @@ cdef class GoldParse:
         self.c.ner = <Transition*>self.mem.alloc(len(doc), sizeof(Transition))
 
         self.cats = {} if cats is None else dict(cats)
+        self.links = links
         self.words = [None] * len(doc)
         self.tags = [None] * len(doc)
         self.heads = [None] * len(doc)

spacy/kb.pxd

@@ -1,53 +1,27 @@
 """Knowledge-base for entity or concept linking."""
 from cymem.cymem cimport Pool
 from preshed.maps cimport PreshMap
+
 from libcpp.vector cimport vector
 from libc.stdint cimport int32_t, int64_t
+from libc.stdio cimport FILE
 
 from spacy.vocab cimport Vocab
 from .typedefs cimport hash_t
 
-
+from .structs cimport KBEntryC, AliasC
-# Internal struct, for storage and disambiguation. This isn't what we return
+ctypedef vector[KBEntryC] entry_vec
-# to the user as the answer to "here's your entity". It's the minimum number
+ctypedef vector[AliasC] alias_vec
-# of bits we need to keep track of the answers.
+ctypedef vector[float] float_vec
-cdef struct _EntryC:
+ctypedef vector[float_vec] float_matrix
-
-    # The hash of this entry's unique ID and name in the kB
-    hash_t entity_hash
-
-    # Allows retrieval of one or more vectors.
-    # Each element of vector_rows should be an index into a vectors table.
-    # Every entry should have the same number of vectors, so we can avoid storing
-    # the number of vectors in each knowledge-base struct
-    int32_t* vector_rows
-
-    # Allows retrieval of a struct of non-vector features. We could make this a
-    # pointer, but we have 32 bits left over in the struct after prob, so we'd
-    # like this to only be 32 bits. We can also set this to -1, for the common
-    # case where there are no features.
-    int32_t feats_row
-
-    # log probability of entity, based on corpus frequency
-    float prob
-
-
-# Each alias struct stores a list of Entry pointers with their prior probabilities
-# for this specific mention/alias.
-cdef struct _AliasC:
-
-    # All entry candidates for this alias
-    vector[int64_t] entry_indices
-
-    # Prior probability P(entity|alias) - should sum up to (at most) 1.
-    vector[float] probs
 
 
 # Object used by the Entity Linker that summarizes one entity-alias candidate combination.
 cdef class Candidate:
-
     cdef readonly KnowledgeBase kb
     cdef hash_t entity_hash
+    cdef float entity_freq
+    cdef vector[float] entity_vector
     cdef hash_t alias_hash
     cdef float prior_prob
 
@@ -55,9 +29,10 @@ cdef class Candidate:
 cdef class KnowledgeBase:
     cdef Pool mem
     cpdef readonly Vocab vocab
+    cdef int64_t entity_vector_length
 
     # This maps 64bit keys (hash of unique entity string)
-    # to 64bit values (position of the _EntryC struct in the _entries vector).
+    # to 64bit values (position of the _KBEntryC struct in the _entries vector).
     # The PreshMap is pretty space efficient, as it uses open addressing. So
     # the only overhead is the vacancy rate, which is approximately 30%.
     cdef PreshMap _entry_index
@@ -66,7 +41,7 @@ cdef class KnowledgeBase:
     # over allocation.
     # In total we end up with (N*128*1.3)+(N*128*1.3) bits for N entries.
     # Storing 1m entries would take 41.6mb under this scheme.
-    cdef vector[_EntryC] _entries
+    cdef entry_vec _entries
 
     # This maps 64bit keys (hash of unique alias string)
     # to 64bit values (position of the _AliasC struct in the _aliases_table vector).
@@ -76,7 +51,7 @@ cdef class KnowledgeBase:
     # should be P(entity | mention), which is pretty important to know.
     # We can pack both pieces of information into a 64-bit value, to keep things
     # efficient.
-    cdef vector[_AliasC] _aliases_table
+    cdef alias_vec _aliases_table
 
     # This is the part which might take more space: storing various
     # categorical features for the entries, and storing vectors for disambiguation
@@ -87,7 +62,7 @@ cdef class KnowledgeBase:
     # model, that embeds different features of the entities into vectors. We'll
     # still want some per-entity features, like the Wikipedia text or entity
     # co-occurrence. Hopefully those vectors can be narrow, e.g. 64 dimensions.
-    cdef object _vectors_table
+    cdef float_matrix _vectors_table
 
     # It's very useful to track categorical features, at least for output, even
     # if they're not useful in the model itself. For instance, we should be
@@ -96,53 +71,102 @@ cdef class KnowledgeBase:
     # optional data, we can let users configure a DB as the backend for this.
     cdef object _features_table
 
+
+    cdef inline int64_t c_add_vector(self, vector[float] entity_vector) nogil:
+        """Add an entity vector to the vectors table."""
+        cdef int64_t new_index = self._vectors_table.size()
+        self._vectors_table.push_back(entity_vector)
+        return new_index
+
+
     cdef inline int64_t c_add_entity(self, hash_t entity_hash, float prob,
-                                     int32_t* vector_rows, int feats_row):
+                                     int32_t vector_index, int feats_row) nogil:
-        """Add an entry to the knowledge base."""
+        """Add an entry to the vector of entries.
-        # This is what we'll map the hash key to. It's where the entry will sit
+        After calling this method, make sure to update also the _entry_index using the return value"""
+        # This is what we'll map the entity hash key to. It's where the entry will sit
         # in the vector of entries, so we can get it later.
         cdef int64_t new_index = self._entries.size()
-        self._entries.push_back(
+
-            _EntryC(
+        # Avoid struct initializer to enable nogil, cf https://github.com/cython/cython/issues/1642
-                entity_hash=entity_hash,
+        cdef KBEntryC entry
-                vector_rows=vector_rows,
+        entry.entity_hash = entity_hash
-                feats_row=feats_row,
+        entry.vector_index = vector_index
-                prob=prob
+        entry.feats_row = feats_row
-            ))
+        entry.prob = prob
-        self._entry_index[entity_hash] = new_index
+
+        self._entries.push_back(entry)
         return new_index
 
-    cdef inline int64_t c_add_aliases(self, hash_t alias_hash, vector[int64_t] entry_indices, vector[float] probs):
+    cdef inline int64_t c_add_aliases(self, hash_t alias_hash, vector[int64_t] entry_indices, vector[float] probs) nogil:
-        """Connect a mention to a list of potential entities with their prior probabilities ."""
+        """Connect a mention to a list of potential entities with their prior probabilities .
+        After calling this method, make sure to update also the _alias_index using the return value"""
+        # This is what we'll map the alias hash key to. It's where the alias will be defined
+        # in the vector of aliases.
         cdef int64_t new_index = self._aliases_table.size()
 
-        self._aliases_table.push_back(
+        # Avoid struct initializer to enable nogil
-            _AliasC(
+        cdef AliasC alias
-                entry_indices=entry_indices,
+        alias.entry_indices = entry_indices
-                probs=probs
+        alias.probs = probs
-            ))
+
-        self._alias_index[alias_hash] = new_index
+        self._aliases_table.push_back(alias)
         return new_index
 
-    cdef inline _create_empty_vectors(self):
+    cdef inline void _create_empty_vectors(self, hash_t dummy_hash) nogil:
         """ 
-        Making sure the first element of each vector is a dummy,
+        Initializing the vectors and making sure the first element of each vector is a dummy,
         because the PreshMap maps pointing to indices in these vectors can not contain 0 as value
         cf. https://github.com/explosion/preshed/issues/17
         """
         cdef int32_t dummy_value = 0
-        self.vocab.strings.add("")
+
-        self._entries.push_back(
+        # Avoid struct initializer to enable nogil
-            _EntryC(
+        cdef KBEntryC entry
-                entity_hash=self.vocab.strings[""],
+        entry.entity_hash = dummy_hash
-                vector_rows=&dummy_value,
+        entry.vector_index = dummy_value
-                feats_row=dummy_value,
+        entry.feats_row = dummy_value
-                prob=dummy_value
+        entry.prob = dummy_value
-            ))
+
-        self._aliases_table.push_back(
+        # Avoid struct initializer to enable nogil
-            _AliasC(
+        cdef vector[int64_t] dummy_entry_indices
-                entry_indices=[dummy_value],
+        dummy_entry_indices.push_back(0)
-                probs=[dummy_value]
+        cdef vector[float] dummy_probs
-            ))
+        dummy_probs.push_back(0)
+
+        cdef AliasC alias
+        alias.entry_indices = dummy_entry_indices
+        alias.probs = dummy_probs
+
+        self._entries.push_back(entry)
+        self._aliases_table.push_back(alias)
+
+    cpdef load_bulk(self, loc)
+    cpdef set_entities(self, entity_list, prob_list, vector_list)
 
 
+cdef class Writer:
+    cdef FILE* _fp
+
+    cdef int write_header(self, int64_t nr_entries, int64_t entity_vector_length) except -1
+    cdef int write_vector_element(self, float element) except -1
+    cdef int write_entry(self, hash_t entry_hash, float entry_prob, int32_t vector_index) except -1
+
+    cdef int write_alias_length(self, int64_t alias_length) except -1
+    cdef int write_alias_header(self, hash_t alias_hash, int64_t candidate_length) except -1
+    cdef int write_alias(self, int64_t entry_index, float prob) except -1
+
+    cdef int _write(self, void* value, size_t size) except -1
+
+cdef class Reader:
+    cdef FILE* _fp
+
+    cdef int read_header(self, int64_t* nr_entries, int64_t* entity_vector_length) except -1
+    cdef int read_vector_element(self, float* element) except -1
+    cdef int read_entry(self, hash_t* entity_hash, float* prob, int32_t* vector_index) except -1
+
+    cdef int read_alias_length(self, int64_t* alias_length) except -1
+    cdef int read_alias_header(self, hash_t* alias_hash, int64_t* candidate_length) except -1
+    cdef int read_alias(self, int64_t* entry_index, float* prob) except -1
+
+    cdef int _read(self, void* value, size_t size) except -1
+

spacy/kb.pyx

@@ -1,13 +1,30 @@
+# cython: infer_types=True
 # cython: profile=True
 # coding: utf8
 from spacy.errors import Errors, Warnings, user_warning
 
+from pathlib import Path
+from cymem.cymem cimport Pool
+from preshed.maps cimport PreshMap
+
+from cpython.exc cimport PyErr_SetFromErrno
+
+from libc.stdio cimport fopen, fclose, fread, fwrite, feof, fseek
+from libc.stdint cimport int32_t, int64_t
+
+from .typedefs cimport hash_t
+
+from os import path
+from libcpp.vector cimport vector
+
 
 cdef class Candidate:
 
-    def __init__(self, KnowledgeBase kb, entity_hash, alias_hash, prior_prob):
+    def __init__(self, KnowledgeBase kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob):
         self.kb = kb
         self.entity_hash = entity_hash
+        self.entity_freq = entity_freq
+        self.entity_vector = entity_vector
         self.alias_hash = alias_hash
         self.prior_prob = prior_prob
 
@@ -19,7 +36,7 @@ cdef class Candidate:
     @property
     def entity_(self):
         """RETURNS (unicode): ID/name of this entity in the KB"""
-        return self.kb.vocab.strings[self.entity]
+        return self.kb.vocab.strings[self.entity_hash]
 
     @property
     def alias(self):
@@ -29,7 +46,15 @@ cdef class Candidate:
     @property
     def alias_(self):
         """RETURNS (unicode): ID of the original alias"""
-        return self.kb.vocab.strings[self.alias]
+        return self.kb.vocab.strings[self.alias_hash]
+
+    @property
+    def entity_freq(self):
+        return self.entity_freq
+
+    @property
+    def entity_vector(self):
+        return self.entity_vector
 
     @property
     def prior_prob(self):
@@ -38,26 +63,41 @@ cdef class Candidate:
 
 cdef class KnowledgeBase:
 
-    def __init__(self, Vocab vocab):
+    def __init__(self, Vocab vocab, entity_vector_length):
         self.vocab = vocab
+        self.mem = Pool()
+        self.entity_vector_length = entity_vector_length
+
         self._entry_index = PreshMap()
         self._alias_index = PreshMap()
-        self.mem = Pool()
+
-        self._create_empty_vectors()
+        self.vocab.strings.add("")
+        self._create_empty_vectors(dummy_hash=self.vocab.strings[""])
+
+    @property
+    def entity_vector_length(self):
+        """RETURNS (uint64): length of the entity vectors"""
+        return self.entity_vector_length
 
     def __len__(self):
         return self.get_size_entities()
 
     def get_size_entities(self):
-        return self._entries.size() - 1  # not counting dummy element on index 0
+        return len(self._entry_index)
+
+    def get_entity_strings(self):
+        return [self.vocab.strings[x] for x in self._entry_index]
 
     def get_size_aliases(self):
-        return self._aliases_table.size() - 1 # not counting dummy element on index 0
+        return len(self._alias_index)
 
-    def add_entity(self, unicode entity, float prob=0.5, vectors=None, features=None):
+    def get_alias_strings(self):
+        return [self.vocab.strings[x] for x in self._alias_index]
+
+    def add_entity(self, unicode entity, float prob, vector[float] entity_vector):
         """
-        Add an entity to the KB.
+        Add an entity to the KB, optionally specifying its log probability based on corpus frequency
-        Return the hash of the entity ID at the end
+        Return the hash of the entity ID/name at the end.
         """
         cdef hash_t entity_hash = self.vocab.strings.add(entity)
 
@@ -66,40 +106,72 @@ cdef class KnowledgeBase:
             user_warning(Warnings.W018.format(entity=entity))
             return
 
-        cdef int32_t dummy_value = 342
+        # Raise an error if the provided entity vector is not of the correct length
-        self.c_add_entity(entity_hash=entity_hash, prob=prob,
+        if len(entity_vector) != self.entity_vector_length:
-                          vector_rows=&dummy_value, feats_row=dummy_value)
+            raise ValueError(Errors.E141.format(found=len(entity_vector), required=self.entity_vector_length))
-        # TODO self._vectors_table.get_pointer(vectors),
+
-        # self._features_table.get(features))
+        vector_index = self.c_add_vector(entity_vector=entity_vector)
+
+        new_index = self.c_add_entity(entity_hash=entity_hash,
+                                      prob=prob,
+                                      vector_index=vector_index,
+                                      feats_row=-1)  # Features table currently not implemented
+        self._entry_index[entity_hash] = new_index
 
         return entity_hash
 
+    cpdef set_entities(self, entity_list, prob_list, vector_list):
+        if len(entity_list) != len(prob_list) or len(entity_list) != len(vector_list):
+            raise ValueError(Errors.E140)
+
+        nr_entities = len(entity_list)
+        self._entry_index = PreshMap(nr_entities+1)
+        self._entries = entry_vec(nr_entities+1)
+
+        i = 0
+        cdef KBEntryC entry
+        while i < nr_entities:
+            entity_vector = vector_list[i]
+            if len(entity_vector) != self.entity_vector_length:
+                raise ValueError(Errors.E141.format(found=len(entity_vector), required=self.entity_vector_length))
+
+            entity_hash = self.vocab.strings.add(entity_list[i])
+            entry.entity_hash = entity_hash
+            entry.prob = prob_list[i]
+
+            vector_index = self.c_add_vector(entity_vector=vector_list[i])
+            entry.vector_index = vector_index
+
+            entry.feats_row = -1   # Features table currently not implemented
+
+            self._entries[i+1] = entry
+            self._entry_index[entity_hash] = i+1
+
+            i += 1
+
     def add_alias(self, unicode alias, entities, probabilities):
         """
         For a given alias, add its potential entities and prior probabilies to the KB.
         Return the alias_hash at the end
         """
-
         # Throw an error if the length of entities and probabilities are not the same
         if not len(entities) == len(probabilities):
             raise ValueError(Errors.E132.format(alias=alias,
                                                 entities_length=len(entities),
                                                 probabilities_length=len(probabilities)))
 
-        # Throw an error if the probabilities sum up to more than 1
+        # Throw an error if the probabilities sum up to more than 1 (allow for some rounding errors)
         prob_sum = sum(probabilities)
-        if prob_sum > 1:
+        if prob_sum > 1.00001:
             raise ValueError(Errors.E133.format(alias=alias, sum=prob_sum))
 
         cdef hash_t alias_hash = self.vocab.strings.add(alias)
 
-        # Return if this alias was added before
+        # Check whether this alias was added before
         if alias_hash in self._alias_index:
             user_warning(Warnings.W017.format(alias=alias))
             return
 
-        cdef hash_t entity_hash
-
         cdef vector[int64_t] entry_indices
         cdef vector[float] probs
 
@@ -112,20 +184,295 @@ cdef class KnowledgeBase:
             entry_indices.push_back(int(entry_index))
             probs.push_back(float(prob))
 
-        self.c_add_aliases(alias_hash=alias_hash, entry_indices=entry_indices, probs=probs)
+        new_index = self.c_add_aliases(alias_hash=alias_hash, entry_indices=entry_indices, probs=probs)
+        self._alias_index[alias_hash] = new_index
 
         return alias_hash
 
-
     def get_candidates(self, unicode alias):
-        """ TODO: where to put this functionality ?"""
         cdef hash_t alias_hash = self.vocab.strings[alias]
         alias_index = <int64_t>self._alias_index.get(alias_hash)
         alias_entry = self._aliases_table[alias_index]
 
         return [Candidate(kb=self,
                           entity_hash=self._entries[entry_index].entity_hash,
+                          entity_freq=self._entries[entry_index].prob,
+                          entity_vector=self._vectors_table[self._entries[entry_index].vector_index],
                           alias_hash=alias_hash,
                           prior_prob=prob)
                 for (entry_index, prob) in zip(alias_entry.entry_indices, alias_entry.probs)
                 if entry_index != 0]
+
+
+    def dump(self, loc):
+        cdef Writer writer = Writer(loc)
+        writer.write_header(self.get_size_entities(), self.entity_vector_length)
+
+        # dumping the entity vectors in their original order
+        i = 0
+        for entity_vector in self._vectors_table:
+            for element in entity_vector:
+                writer.write_vector_element(element)
+            i = i+1
+
+        # dumping the entry records in the order in which they are in the _entries vector.
+        # index 0 is a dummy object not stored in the _entry_index and can be ignored.
+        i = 1
+        for entry_hash, entry_index in sorted(self._entry_index.items(), key=lambda x: x[1]):
+            entry = self._entries[entry_index]
+            assert entry.entity_hash == entry_hash
+            assert entry_index == i
+            writer.write_entry(entry.entity_hash, entry.prob, entry.vector_index)
+            i = i+1
+
+        writer.write_alias_length(self.get_size_aliases())
+
+        # dumping the aliases in the order in which they are in the _alias_index vector.
+        # index 0 is a dummy object not stored in the _aliases_table and can be ignored.
+        i = 1
+        for alias_hash, alias_index in sorted(self._alias_index.items(), key=lambda x: x[1]):
+            alias = self._aliases_table[alias_index]
+            assert alias_index == i
+
+            candidate_length = len(alias.entry_indices)
+            writer.write_alias_header(alias_hash, candidate_length)
+
+            for j in range(0, candidate_length):
+                writer.write_alias(alias.entry_indices[j], alias.probs[j])
+
+            i = i+1
+
+        writer.close()
+
+    cpdef load_bulk(self, loc):
+        cdef hash_t entity_hash
+        cdef hash_t alias_hash
+        cdef int64_t entry_index
+        cdef float prob
+        cdef int32_t vector_index
+        cdef KBEntryC entry
+        cdef AliasC alias
+        cdef float vector_element
+
+        cdef Reader reader = Reader(loc)
+
+        # STEP 0: load header and initialize KB
+        cdef int64_t nr_entities
+        cdef int64_t entity_vector_length
+        reader.read_header(&nr_entities, &entity_vector_length)
+
+        self.entity_vector_length = entity_vector_length
+        self._entry_index = PreshMap(nr_entities+1)
+        self._entries = entry_vec(nr_entities+1)
+        self._vectors_table = float_matrix(nr_entities+1)
+
+        # STEP 1: load entity vectors
+        cdef int i = 0
+        cdef int j = 0
+        while i < nr_entities:
+            entity_vector = float_vec(entity_vector_length)
+            j = 0
+            while j < entity_vector_length:
+                reader.read_vector_element(&vector_element)
+                entity_vector[j] = vector_element
+                j = j+1
+            self._vectors_table[i] = entity_vector
+            i = i+1
+
+        # STEP 2: load entities
+        # we assume that the entity data was written in sequence
+        # index 0 is a dummy object not stored in the _entry_index and can be ignored.
+        i = 1
+        while i <= nr_entities:
+            reader.read_entry(&entity_hash, &prob, &vector_index)
+
+            entry.entity_hash = entity_hash
+            entry.prob = prob
+            entry.vector_index = vector_index
+            entry.feats_row = -1    # Features table currently not implemented
+
+            self._entries[i] = entry
+            self._entry_index[entity_hash] = i
+
+            i += 1
+
+        # check that all entities were read in properly
+        assert nr_entities == self.get_size_entities()
+
+        # STEP 3: load aliases
+
+        cdef int64_t nr_aliases
+        reader.read_alias_length(&nr_aliases)
+        self._alias_index = PreshMap(nr_aliases+1)
+        self._aliases_table = alias_vec(nr_aliases+1)
+
+        cdef int64_t nr_candidates
+        cdef vector[int64_t] entry_indices
+        cdef vector[float] probs
+
+        i = 1
+        # we assume the alias data was written in sequence
+        # index 0 is a dummy object not stored in the _entry_index and can be ignored.
+        while i <= nr_aliases:
+            reader.read_alias_header(&alias_hash, &nr_candidates)
+            entry_indices = vector[int64_t](nr_candidates)
+            probs = vector[float](nr_candidates)
+
+            for j in range(0, nr_candidates):
+                reader.read_alias(&entry_index, &prob)
+                entry_indices[j] = entry_index
+                probs[j] = prob
+
+            alias.entry_indices = entry_indices
+            alias.probs = probs
+
+            self._aliases_table[i] = alias
+            self._alias_index[alias_hash] = i
+
+            i += 1
+
+        # check that all aliases were read in properly
+        assert nr_aliases == self.get_size_aliases()
+
+
+cdef class Writer:
+    def __init__(self, object loc):
+        if path.exists(loc):
+            assert not path.isdir(loc), "%s is directory." % loc
+        if isinstance(loc, Path):
+            loc = bytes(loc)
+        cdef bytes bytes_loc = loc.encode('utf8') if type(loc) == unicode else loc
+        self._fp = fopen(<char*>bytes_loc, 'wb')
+        assert self._fp != NULL
+        fseek(self._fp, 0, 0)
+
+    def close(self):
+        cdef size_t status = fclose(self._fp)
+        assert status == 0
+
+    cdef int write_header(self, int64_t nr_entries, int64_t entity_vector_length) except -1:
+        self._write(&nr_entries, sizeof(nr_entries))
+        self._write(&entity_vector_length, sizeof(entity_vector_length))
+
+    cdef int write_vector_element(self, float element) except -1:
+        self._write(&element, sizeof(element))
+
+    cdef int write_entry(self, hash_t entry_hash, float entry_prob, int32_t vector_index) except -1:
+        self._write(&entry_hash, sizeof(entry_hash))
+        self._write(&entry_prob, sizeof(entry_prob))
+        self._write(&vector_index, sizeof(vector_index))
+        # Features table currently not implemented and not written to file
+
+    cdef int write_alias_length(self, int64_t alias_length) except -1:
+        self._write(&alias_length, sizeof(alias_length))
+
+    cdef int write_alias_header(self, hash_t alias_hash, int64_t candidate_length) except -1:
+        self._write(&alias_hash, sizeof(alias_hash))
+        self._write(&candidate_length, sizeof(candidate_length))
+
+    cdef int write_alias(self, int64_t entry_index, float prob) except -1:
+        self._write(&entry_index, sizeof(entry_index))
+        self._write(&prob, sizeof(prob))
+
+    cdef int _write(self, void* value, size_t size) except -1:
+        status = fwrite(value, size, 1, self._fp)
+        assert status == 1, status
+
+
+cdef class Reader:
+    def __init__(self, object loc):
+        assert path.exists(loc)
+        assert not path.isdir(loc)
+        if isinstance(loc, Path):
+            loc = bytes(loc)
+        cdef bytes bytes_loc = loc.encode('utf8') if type(loc) == unicode else loc
+        self._fp = fopen(<char*>bytes_loc, 'rb')
+        if not self._fp:
+            PyErr_SetFromErrno(IOError)
+        status = fseek(self._fp, 0, 0)  # this can be 0 if there is no header
+
+    def __dealloc__(self):
+        fclose(self._fp)
+
+    cdef int read_header(self, int64_t* nr_entries, int64_t* entity_vector_length) except -1:
+        status = self._read(nr_entries, sizeof(int64_t))
+        if status < 1:
+            if feof(self._fp):
+                return 0  # end of file
+            raise IOError("error reading header from input file")
+
+        status = self._read(entity_vector_length, sizeof(int64_t))
+        if status < 1:
+            if feof(self._fp):
+                return 0  # end of file
+            raise IOError("error reading header from input file")
+
+    cdef int read_vector_element(self, float* element) except -1:
+        status = self._read(element, sizeof(float))
+        if status < 1:
+            if feof(self._fp):
+                return 0  # end of file
+            raise IOError("error reading entity vector from input file")
+
+    cdef int read_entry(self, hash_t* entity_hash, float* prob, int32_t* vector_index) except -1:
+        status = self._read(entity_hash, sizeof(hash_t))
+        if status < 1:
+            if feof(self._fp):
+                return 0  # end of file
+            raise IOError("error reading entity hash from input file")
+
+        status = self._read(prob, sizeof(float))
+        if status < 1:
+            if feof(self._fp):
+                return 0  # end of file
+            raise IOError("error reading entity prob from input file")
+
+        status = self._read(vector_index, sizeof(int32_t))
+        if status < 1:
+            if feof(self._fp):
+                return 0  # end of file
+            raise IOError("error reading entity vector from input file")
+
+        if feof(self._fp):
+            return 0
+        else:
+            return 1
+
+    cdef int read_alias_length(self, int64_t* alias_length) except -1:
+        status = self._read(alias_length, sizeof(int64_t))
+        if status < 1:
+            if feof(self._fp):
+                return 0  # end of file
+            raise IOError("error reading alias length from input file")
+
+    cdef int read_alias_header(self, hash_t* alias_hash, int64_t* candidate_length) except -1:
+        status = self._read(alias_hash, sizeof(hash_t))
+        if status < 1:
+            if feof(self._fp):
+                return 0  # end of file
+            raise IOError("error reading alias hash from input file")
+
+        status = self._read(candidate_length, sizeof(int64_t))
+        if status < 1:
+            if feof(self._fp):
+                return 0  # end of file
+            raise IOError("error reading candidate length from input file")
+
+    cdef int read_alias(self, int64_t* entry_index, float* prob) except -1:
+        status = self._read(entry_index, sizeof(int64_t))
+        if status < 1:
+            if feof(self._fp):
+                return 0  # end of file
+            raise IOError("error reading entry index for alias from input file")
+
+        status = self._read(prob, sizeof(float))
+        if status < 1:
+            if feof(self._fp):
+                return 0  # end of file
+            raise IOError("error reading prob for entity/alias from input file")
+
+    cdef int _read(self, void* value, size_t size) except -1:
+        status = fread(value, size, 1, self._fp)
+        return status
+
+

spacy/pipeline/pipes.pyx

@@ -3,16 +3,18 @@
 # coding: utf8
 from __future__ import unicode_literals
 
-cimport numpy as np
-
 import numpy
 import srsly
+import random
 from collections import OrderedDict
 from thinc.api import chain
 from thinc.v2v import Affine, Maxout, Softmax
 from thinc.misc import LayerNorm
-from thinc.neural.util import to_categorical, copy_array
+from thinc.neural.util import to_categorical
+from thinc.neural.util import get_array_module
 
+from spacy.kb import KnowledgeBase
+from ..cli.pretrain import get_cossim_loss
 from .functions import merge_subtokens
 from ..tokens.doc cimport Doc
 from ..syntax.nn_parser cimport Parser
@@ -24,9 +26,9 @@ from ..vocab cimport Vocab
 from ..syntax import nonproj
 from ..attrs import POS, ID
 from ..parts_of_speech import X
-from .._ml import Tok2Vec, build_tagger_model
+from .._ml import Tok2Vec, build_tagger_model, cosine
 from .._ml import build_text_classifier, build_simple_cnn_text_classifier
-from .._ml import build_bow_text_classifier
+from .._ml import build_bow_text_classifier, build_nel_encoder
 from .._ml import link_vectors_to_models, zero_init, flatten
 from .._ml import masked_language_model, create_default_optimizer
 from ..errors import Errors, TempErrors
@@ -229,7 +231,7 @@ class Tensorizer(Pipe):
 
         vocab (Vocab): A `Vocab` instance. The model must share the same
             `Vocab` instance with the `Doc` objects it will process.
-        model (Model): A `Model` instance or `True` allocate one later.
+        model (Model): A `Model` instance or `True` to allocate one later.
         **cfg: Config parameters.
 
         EXAMPLE:
@@ -294,7 +296,7 @@ class Tensorizer(Pipe):
 
         docs (iterable): A batch of `Doc` objects.
         golds (iterable): A batch of `GoldParse` objects.
-        drop (float): The droput rate.
+        drop (float): The dropout rate.
         sgd (callable): An optimizer.
         RETURNS (dict): Results from the update.
         """
@@ -386,7 +388,7 @@ class Tagger(Pipe):
     def predict(self, docs):
         self.require_model()
         if not any(len(doc) for doc in docs):
-            # Handle case where there are no tokens in any docs.
+            # Handle cases where there are no tokens in any docs.
             n_labels = len(self.labels)
             guesses = [self.model.ops.allocate((0, n_labels)) for doc in docs]
             tokvecs = self.model.ops.allocate((0, self.model.tok2vec.nO))
@@ -1063,52 +1065,252 @@ cdef class EntityRecognizer(Parser):
 
 
 class EntityLinker(Pipe):
+    """Pipeline component for named entity linking.
+
+    DOCS: TODO
+    """
     name = 'entity_linker'
 
     @classmethod
-    def Model(cls, nr_class=1, **cfg):
+    def Model(cls, **cfg):
-        # TODO: non-dummy EL implementation
+        embed_width = cfg.get("embed_width", 300)
-        return None
+        hidden_width = cfg.get("hidden_width", 128)
+        type_to_int = cfg.get("type_to_int", dict())
 
-    def __init__(self, model=True, **cfg):
+        model = build_nel_encoder(embed_width=embed_width, hidden_width=hidden_width, ner_types=len(type_to_int), **cfg)
-        self.model = False
+        return model
+
+    def __init__(self, vocab, **cfg):
+        self.vocab = vocab
+        self.model = True
+        self.kb = None
         self.cfg = dict(cfg)
-        self.kb = self.cfg["kb"]
+        self.sgd_context = None
+
+    def set_kb(self, kb):
+        self.kb = kb
+
+    def require_model(self):
+        # Raise an error if the component's model is not initialized.
+        if getattr(self, "model", None) in (None, True, False):
+            raise ValueError(Errors.E109.format(name=self.name))
+
+    def require_kb(self):
+        # Raise an error if the knowledge base is not initialized.
+        if getattr(self, "kb", None) in (None, True, False):
+            raise ValueError(Errors.E139.format(name=self.name))
+
+    def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None, **kwargs):
+        self.require_kb()
+        self.cfg["entity_width"] = self.kb.entity_vector_length
+
+        if self.model is True:
+            self.model = self.Model(**self.cfg)
+            self.sgd_context = self.create_optimizer()
+
+        if sgd is None:
+            sgd = self.create_optimizer()
+
+        return sgd
+
+    def update(self, docs, golds, state=None, drop=0.0, sgd=None, losses=None):
+        self.require_model()
+        self.require_kb()
+
+        if losses is not None:
+            losses.setdefault(self.name, 0.0)
+
+        if not docs or not golds:
+            return 0
+
+        if len(docs) != len(golds):
+            raise ValueError(Errors.E077.format(value="EL training", n_docs=len(docs),
+                                                n_golds=len(golds)))
+
+        if isinstance(docs, Doc):
+            docs = [docs]
+            golds = [golds]
+
+        context_docs = []
+        entity_encodings = []
+        cats = []
+        priors = []
+        type_vectors = []
+
+        type_to_int = self.cfg.get("type_to_int", dict())
+
+        for doc, gold in zip(docs, golds):
+            ents_by_offset = dict()
+            for ent in doc.ents:
+                ents_by_offset[str(ent.start_char) + "_" + str(ent.end_char)] = ent
+            for entity in gold.links:
+                start, end, gold_kb = entity
+                mention = doc.text[start:end]
+
+                gold_ent = ents_by_offset[str(ent.start_char) + "_" + str(ent.end_char)]
+                assert gold_ent is not None
+                type_vector = [0 for i in range(len(type_to_int))]
+                if len(type_to_int) > 0:
+                    type_vector[type_to_int[gold_ent.label_]] = 1
+
+                candidates = self.kb.get_candidates(mention)
+                random.shuffle(candidates)
+                nr_neg = 0
+                for c in candidates:
+                    kb_id = c.entity_
+                    entity_encoding = c.entity_vector
+                    entity_encodings.append(entity_encoding)
+                    context_docs.append(doc)
+                    type_vectors.append(type_vector)
+
+                    if self.cfg.get("prior_weight", 1) > 0:
+                        priors.append([c.prior_prob])
+                    else:
+                        priors.append([0])
+
+                    if kb_id == gold_kb:
+                        cats.append([1])
+                    else:
+                        nr_neg += 1
+                        cats.append([0])
+
+        if len(entity_encodings) > 0:
+            assert len(priors) == len(entity_encodings) == len(context_docs) == len(cats) == len(type_vectors)
+
+            context_encodings, bp_context = self.model.tok2vec.begin_update(context_docs, drop=drop)
+            entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32")
+
+            mention_encodings = [list(context_encodings[i]) + list(entity_encodings[i]) + priors[i] + type_vectors[i]
+                                 for i in range(len(entity_encodings))]
+            pred, bp_mention = self.model.begin_update(self.model.ops.asarray(mention_encodings, dtype="float32"), drop=drop)
+            cats = self.model.ops.asarray(cats, dtype="float32")
+
+            loss, d_scores = self.get_loss(prediction=pred, golds=cats, docs=None)
+            mention_gradient = bp_mention(d_scores, sgd=sgd)
+
+            context_gradients = [list(x[0:self.cfg.get("context_width")]) for x in mention_gradient]
+            bp_context(self.model.ops.asarray(context_gradients, dtype="float32"), sgd=self.sgd_context)
+
+            if losses is not None:
+                losses[self.name] += loss
+            return loss
+        return 0
+
+    def get_loss(self, docs, golds, prediction):
+        d_scores = (prediction - golds)
+        loss = (d_scores ** 2).sum()
+        loss = loss / len(golds)
+        return loss, d_scores
+
+    def get_loss_old(self, docs, golds, scores):
+        # this loss function assumes we're only using positive examples
+        loss, gradients = get_cossim_loss(yh=scores, y=golds)
+        loss = loss / len(golds)
+        return loss, gradients
 
     def __call__(self, doc):
-        self.set_annotations([doc], scores=None, tensors=None)
+        entities, kb_ids = self.predict([doc])
+        self.set_annotations([doc], entities, kb_ids)
         return doc
 
     def pipe(self, stream, batch_size=128, n_threads=-1):
-        """Apply the pipe to a stream of documents.
-        Both __call__ and pipe should delegate to the `predict()`
-        and `set_annotations()` methods.
-        """
         for docs in util.minibatch(stream, size=batch_size):
             docs = list(docs)
-            self.set_annotations(docs, scores=None, tensors=None)
+            entities, kb_ids = self.predict(docs)
+            self.set_annotations(docs, entities, kb_ids)
             yield from docs
 
-    def set_annotations(self, docs, scores, tensors=None):
+    def predict(self, docs):
-        """
+        self.require_model()
-        Currently implemented as taking the KB entry with highest prior probability for each named entity
+        self.require_kb()
-        TODO: actually use context etc
+
-        """
+        final_entities = []
+        final_kb_ids = []
+
+        if not docs:
+            return final_entities, final_kb_ids
+
+        if isinstance(docs, Doc):
+            docs = [docs]
+
+        context_encodings = self.model.tok2vec(docs)
+        xp = get_array_module(context_encodings)
+
+        type_to_int = self.cfg.get("type_to_int", dict())
+
         for i, doc in enumerate(docs):
+            if len(doc) > 0:
+                context_encoding = context_encodings[i]
                 for ent in doc.ents:
+                    type_vector = [0 for i in range(len(type_to_int))]
+                    if len(type_to_int) > 0:
+                        type_vector[type_to_int[ent.label_]] = 1
+
                     candidates = self.kb.get_candidates(ent.text)
                     if candidates:
-                    best_candidate = max(candidates, key=lambda c: c.prior_prob)
+                        random.shuffle(candidates)
-                    for token in ent:
-                        token.ent_kb_id_ = best_candidate.entity_
 
-    def get_loss(self, docs, golds, scores):
+                        # this will set the prior probabilities to 0 (just like in training) if their weight is 0
-        # TODO
+                        prior_probs = xp.asarray([[c.prior_prob] for c in candidates])
-        pass
+                        prior_probs *= self.cfg.get("prior_weight", 1)
+                        scores = prior_probs
+
+                        if self.cfg.get("context_weight", 1) > 0:
+                            entity_encodings = xp.asarray([c.entity_vector for c in candidates])
+                            assert len(entity_encodings) == len(prior_probs)
+                            mention_encodings = [list(context_encoding) + list(entity_encodings[i])
+                                                 + list(prior_probs[i]) + type_vector
+                                                 for i in range(len(entity_encodings))]
+                            scores = self.model(self.model.ops.asarray(mention_encodings, dtype="float32"))
+
+                        # TODO: thresholding
+                        best_index = scores.argmax()
+                        best_candidate = candidates[best_index]
+                        final_entities.append(ent)
+                        final_kb_ids.append(best_candidate.entity_)
+
+        return final_entities, final_kb_ids
+
+    def set_annotations(self, docs, entities, kb_ids=None):
+        for entity, kb_id in zip(entities, kb_ids):
+            for token in entity:
+                token.ent_kb_id_ = kb_id
+
+    def to_disk(self, path, exclude=tuple(), **kwargs):
+        serialize = OrderedDict()
+        serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
+        serialize["vocab"] = lambda p: self.vocab.to_disk(p)
+        serialize["kb"] = lambda p: self.kb.dump(p)
+        if self.model not in (None, True, False):
+            serialize["model"] = lambda p: p.open("wb").write(self.model.to_bytes())
+        exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
+        util.to_disk(path, serialize, exclude)
+
+    def from_disk(self, path, exclude=tuple(), **kwargs):
+        def load_model(p):
+             if self.model is True:
+                self.model = self.Model(**self.cfg)
+             self.model.from_bytes(p.open("rb").read())
+
+        def load_kb(p):
+            kb = KnowledgeBase(vocab=self.vocab, entity_vector_length=self.cfg["entity_width"])
+            kb.load_bulk(p)
+            self.set_kb(kb)
+
+        deserialize = OrderedDict()
+        deserialize["cfg"] = lambda p: self.cfg.update(_load_cfg(p))
+        deserialize["vocab"] = lambda p: self.vocab.from_disk(p)
+        deserialize["kb"] = load_kb
+        deserialize["model"] = load_model
+        exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
+        util.from_disk(path, deserialize, exclude)
+        return self
+
+    def rehearse(self, docs, sgd=None, losses=None, **config):
+        raise NotImplementedError
 
     def add_label(self, label):
-        # TODO
+        raise NotImplementedError
-        pass
 
 
 class Sentencizer(object):

spacy/structs.pxd

@@ -3,6 +3,10 @@ from libc.stdint cimport uint8_t, uint32_t, int32_t, uint64_t
 from .typedefs cimport flags_t, attr_t, hash_t
 from .parts_of_speech cimport univ_pos_t
 
+from libcpp.vector cimport vector
+from libc.stdint cimport int32_t, int64_t
+
+
 
 cdef struct LexemeC:
     flags_t flags
@@ -72,3 +76,32 @@ cdef struct TokenC:
     attr_t ent_type # TODO: Is there a better way to do this? Multiple sources of truth..
     attr_t ent_kb_id
     hash_t ent_id
+
+
+# Internal struct, for storage and disambiguation of entities.
+cdef struct KBEntryC:
+
+    # The hash of this entry's unique ID/name in the kB
+    hash_t entity_hash
+
+    # Allows retrieval of the entity vector, as an index into a vectors table of the KB.
+    # Can be expanded later to refer to multiple rows (compositional model to reduce storage footprint).
+    int32_t vector_index
+
+    # Allows retrieval of a struct of non-vector features.
+    # This is currently not implemented and set to -1 for the common case where there are no features.
+    int32_t feats_row
+
+    # log probability of entity, based on corpus frequency
+    float prob
+
+
+# Each alias struct stores a list of Entry pointers with their prior probabilities
+# for this specific mention/alias.
+cdef struct AliasC:
+
+    # All entry candidates for this alias
+    vector[int64_t] entry_indices
+
+    # Prior probability P(entity|alias) - should sum up to (at most) 1.
+    vector[float] probs

spacy/symbols.pxd

@@ -81,6 +81,7 @@ cdef enum symbol_t:
     DEP
     ENT_IOB
     ENT_TYPE
+    ENT_KB_ID
     HEAD
     SENT_START
     SPACY

spacy/symbols.pyx

@@ -86,6 +86,7 @@ IDS = {
     "DEP": DEP,
     "ENT_IOB": ENT_IOB,
     "ENT_TYPE": ENT_TYPE,
+    "ENT_KB_ID": ENT_KB_ID,
     "HEAD": HEAD,
     "SENT_START": SENT_START,
     "SPACY": SPACY,

spacy/tests/pipeline/test_el.py

@@ -1,91 +0,0 @@
-# coding: utf-8
-from __future__ import unicode_literals
-
-import pytest
-
-from spacy.kb import KnowledgeBase
-from spacy.lang.en import English
-
-
-@pytest.fixture
-def nlp():
-    return English()
-
-
-def test_kb_valid_entities(nlp):
-    """Test the valid construction of a KB with 3 entities and two aliases"""
-    mykb = KnowledgeBase(nlp.vocab)
-
-    # adding entities
-    mykb.add_entity(entity=u'Q1', prob=0.9)
-    mykb.add_entity(entity=u'Q2')
-    mykb.add_entity(entity=u'Q3', prob=0.5)
-
-    # adding aliases
-    mykb.add_alias(alias=u'douglas', entities=[u'Q2', u'Q3'], probabilities=[0.8, 0.2])
-    mykb.add_alias(alias=u'adam', entities=[u'Q2'], probabilities=[0.9])
-
-    # test the size of the corresponding KB
-    assert(mykb.get_size_entities() == 3)
-    assert(mykb.get_size_aliases() == 2)
-
-
-def test_kb_invalid_entities(nlp):
-    """Test the invalid construction of a KB with an alias linked to a non-existing entity"""
-    mykb = KnowledgeBase(nlp.vocab)
-
-    # adding entities
-    mykb.add_entity(entity=u'Q1', prob=0.9)
-    mykb.add_entity(entity=u'Q2', prob=0.2)
-    mykb.add_entity(entity=u'Q3', prob=0.5)
-
-    # adding aliases - should fail because one of the given IDs is not valid
-    with pytest.raises(ValueError):
-        mykb.add_alias(alias=u'douglas', entities=[u'Q2', u'Q342'], probabilities=[0.8, 0.2])
-
-
-def test_kb_invalid_probabilities(nlp):
-    """Test the invalid construction of a KB with wrong prior probabilities"""
-    mykb = KnowledgeBase(nlp.vocab)
-
-    # adding entities
-    mykb.add_entity(entity=u'Q1', prob=0.9)
-    mykb.add_entity(entity=u'Q2', prob=0.2)
-    mykb.add_entity(entity=u'Q3', prob=0.5)
-
-    # adding aliases - should fail because the sum of the probabilities exceeds 1
-    with pytest.raises(ValueError):
-        mykb.add_alias(alias=u'douglas', entities=[u'Q2', u'Q3'], probabilities=[0.8, 0.4])
-
-
-def test_kb_invalid_combination(nlp):
-    """Test the invalid construction of a KB with non-matching entity and probability lists"""
-    mykb = KnowledgeBase(nlp.vocab)
-
-    # adding entities
-    mykb.add_entity(entity=u'Q1', prob=0.9)
-    mykb.add_entity(entity=u'Q2', prob=0.2)
-    mykb.add_entity(entity=u'Q3', prob=0.5)
-
-    # adding aliases - should fail because the entities and probabilities vectors are not of equal length
-    with pytest.raises(ValueError):
-        mykb.add_alias(alias=u'douglas', entities=[u'Q2', u'Q3'], probabilities=[0.3, 0.4, 0.1])
-
-
-def test_candidate_generation(nlp):
-    """Test correct candidate generation"""
-    mykb = KnowledgeBase(nlp.vocab)
-
-    # adding entities
-    mykb.add_entity(entity=u'Q1', prob=0.9)
-    mykb.add_entity(entity=u'Q2', prob=0.2)
-    mykb.add_entity(entity=u'Q3', prob=0.5)
-
-    # adding aliases
-    mykb.add_alias(alias=u'douglas', entities=[u'Q2', u'Q3'], probabilities=[0.8, 0.2])
-    mykb.add_alias(alias=u'adam', entities=[u'Q2'], probabilities=[0.9])
-
-    # test the size of the relevant candidates
-    assert(len(mykb.get_candidates(u'douglas')) == 2)
-    assert(len(mykb.get_candidates(u'adam')) == 1)
-    assert(len(mykb.get_candidates(u'shrubbery')) == 0)

spacy/tests/pipeline/test_entity_linker.py

@@ -0,0 +1,145 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import pytest
+
+from spacy.kb import KnowledgeBase
+from spacy.lang.en import English
+from spacy.pipeline import EntityRuler
+
+
+@pytest.fixture
+def nlp():
+    return English()
+
+
+def test_kb_valid_entities(nlp):
+    """Test the valid construction of a KB with 3 entities and two aliases"""
+    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+
+    # adding entities
+    mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
+    mykb.add_entity(entity='Q2', prob=0.5, entity_vector=[2])
+    mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
+
+    # adding aliases
+    mykb.add_alias(alias='douglas', entities=['Q2', 'Q3'], probabilities=[0.8, 0.2])
+    mykb.add_alias(alias='adam', entities=['Q2'], probabilities=[0.9])
+
+    # test the size of the corresponding KB
+    assert(mykb.get_size_entities() == 3)
+    assert(mykb.get_size_aliases() == 2)
+
+
+def test_kb_invalid_entities(nlp):
+    """Test the invalid construction of a KB with an alias linked to a non-existing entity"""
+    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+
+    # adding entities
+    mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
+    mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
+    mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
+
+    # adding aliases - should fail because one of the given IDs is not valid
+    with pytest.raises(ValueError):
+        mykb.add_alias(alias='douglas', entities=['Q2', 'Q342'], probabilities=[0.8, 0.2])
+
+
+def test_kb_invalid_probabilities(nlp):
+    """Test the invalid construction of a KB with wrong prior probabilities"""
+    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+
+    # adding entities
+    mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
+    mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
+    mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
+
+    # adding aliases - should fail because the sum of the probabilities exceeds 1
+    with pytest.raises(ValueError):
+        mykb.add_alias(alias='douglas', entities=['Q2', 'Q3'], probabilities=[0.8, 0.4])
+
+
+def test_kb_invalid_combination(nlp):
+    """Test the invalid construction of a KB with non-matching entity and probability lists"""
+    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+
+    # adding entities
+    mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
+    mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
+    mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
+
+    # adding aliases - should fail because the entities and probabilities vectors are not of equal length
+    with pytest.raises(ValueError):
+        mykb.add_alias(alias='douglas', entities=['Q2', 'Q3'], probabilities=[0.3, 0.4, 0.1])
+
+
+def test_kb_invalid_entity_vector(nlp):
+    """Test the invalid construction of a KB with non-matching entity vector lengths"""
+    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
+
+    # adding entities
+    mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1, 2, 3])
+
+    # this should fail because the kb's expected entity vector length is 3
+    with pytest.raises(ValueError):
+        mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
+
+
+def test_candidate_generation(nlp):
+    """Test correct candidate generation"""
+    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+
+    # adding entities
+    mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
+    mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
+    mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
+
+    # adding aliases
+    mykb.add_alias(alias='douglas', entities=['Q2', 'Q3'], probabilities=[0.8, 0.2])
+    mykb.add_alias(alias='adam', entities=['Q2'], probabilities=[0.9])
+
+    # test the size of the relevant candidates
+    assert(len(mykb.get_candidates('douglas')) == 2)
+    assert(len(mykb.get_candidates('adam')) == 1)
+    assert(len(mykb.get_candidates('shrubbery')) == 0)
+
+
+def test_preserving_links_asdoc(nlp):
+    """Test that Span.as_doc preserves the existing entity links"""
+    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+
+    # adding entities
+    mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
+    mykb.add_entity(entity='Q2', prob=0.8, entity_vector=[1])
+
+    # adding aliases
+    mykb.add_alias(alias='Boston', entities=['Q1'], probabilities=[0.7])
+    mykb.add_alias(alias='Denver', entities=['Q2'], probabilities=[0.6])
+
+    # set up pipeline with NER (Entity Ruler) and NEL (prior probability only, model not trained)
+    sentencizer = nlp.create_pipe("sentencizer")
+    nlp.add_pipe(sentencizer)
+
+    ruler = EntityRuler(nlp)
+    patterns = [{"label": "GPE", "pattern": "Boston"},
+                {"label": "GPE", "pattern": "Denver"}]
+    ruler.add_patterns(patterns)
+    nlp.add_pipe(ruler)
+
+    el_pipe = nlp.create_pipe(name='entity_linker', config={"context_width": 64})
+    el_pipe.set_kb(mykb)
+    el_pipe.begin_training()
+    el_pipe.context_weight = 0
+    el_pipe.prior_weight = 1
+    nlp.add_pipe(el_pipe, last=True)
+
+    # test whether the entity links are preserved by the `as_doc()` function
+    text = "She lives in Boston. He lives in Denver."
+    doc = nlp(text)
+    for ent in doc.ents:
+        orig_text = ent.text
+        orig_kb_id = ent.kb_id_
+        sent_doc = ent.sent.as_doc()
+        for s_ent in sent_doc.ents:
+            if s_ent.text == orig_text:
+                assert s_ent.kb_id_ == orig_kb_id

spacy/tests/serialize/test_serialize_kb.py

@@ -0,0 +1,74 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+from ..util import make_tempdir
+from ...util import ensure_path
+
+from spacy.kb import KnowledgeBase
+
+
+def test_serialize_kb_disk(en_vocab):
+    # baseline assertions
+    kb1 = _get_dummy_kb(en_vocab)
+    _check_kb(kb1)
+
+    # dumping to file & loading back in
+    with make_tempdir() as d:
+        dir_path = ensure_path(d)
+        if not dir_path.exists():
+            dir_path.mkdir()
+        file_path = dir_path / "kb"
+        kb1.dump(str(file_path))
+
+        kb2 = KnowledgeBase(vocab=en_vocab, entity_vector_length=3)
+        kb2.load_bulk(str(file_path))
+
+    # final assertions
+    _check_kb(kb2)
+
+
+def _get_dummy_kb(vocab):
+    kb = KnowledgeBase(vocab=vocab, entity_vector_length=3)
+
+    kb.add_entity(entity='Q53', prob=0.33, entity_vector=[0, 5, 3])
+    kb.add_entity(entity='Q17', prob=0.2, entity_vector=[7, 1, 0])
+    kb.add_entity(entity='Q007', prob=0.7, entity_vector=[0, 0, 7])
+    kb.add_entity(entity='Q44', prob=0.4, entity_vector=[4, 4, 4])
+
+    kb.add_alias(alias='double07', entities=['Q17', 'Q007'], probabilities=[0.1, 0.9])
+    kb.add_alias(alias='guy', entities=['Q53', 'Q007', 'Q17', 'Q44'], probabilities=[0.3, 0.3, 0.2, 0.1])
+    kb.add_alias(alias='random', entities=['Q007'], probabilities=[1.0])
+
+    return kb
+
+
+def _check_kb(kb):
+    # check entities
+    assert kb.get_size_entities() == 4
+    for entity_string in ['Q53', 'Q17', 'Q007', 'Q44']:
+        assert entity_string in kb.get_entity_strings()
+    for entity_string in ['', 'Q0']:
+        assert entity_string not in kb.get_entity_strings()
+
+    # check aliases
+    assert kb.get_size_aliases() == 3
+    for alias_string in ['double07', 'guy', 'random']:
+        assert alias_string in kb.get_alias_strings()
+    for alias_string in ['nothingness', '', 'randomnoise']:
+        assert alias_string not in kb.get_alias_strings()
+
+    # check candidates & probabilities
+    candidates = sorted(kb.get_candidates('double07'), key=lambda x: x.entity_)
+    assert len(candidates) == 2
+
+    assert candidates[0].entity_ == 'Q007'
+    assert 0.6999 < candidates[0].entity_freq < 0.701
+    assert candidates[0].entity_vector == [0, 0, 7]
+    assert candidates[0].alias_ == 'double07'
+    assert 0.899 < candidates[0].prior_prob < 0.901
+
+    assert candidates[1].entity_ == 'Q17'
+    assert 0.199 < candidates[1].entity_freq < 0.201
+    assert candidates[1].entity_vector == [7, 1, 0]
+    assert candidates[1].alias_ == 'double07'
+    assert 0.099 < candidates[1].prior_prob < 0.101

spacy/tokens/doc.pyx

@@ -22,7 +22,7 @@ from ..lexeme cimport Lexeme, EMPTY_LEXEME
 from ..typedefs cimport attr_t, flags_t
 from ..attrs cimport ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, CLUSTER
 from ..attrs cimport LENGTH, POS, LEMMA, TAG, DEP, HEAD, SPACY, ENT_IOB
-from ..attrs cimport ENT_TYPE, SENT_START, attr_id_t
+from ..attrs cimport ENT_TYPE, ENT_KB_ID, SENT_START, attr_id_t
 from ..parts_of_speech cimport CCONJ, PUNCT, NOUN, univ_pos_t
 
 from ..attrs import intify_attrs, IDS
@@ -64,6 +64,8 @@ cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) nogil:
         return token.ent_iob
     elif feat_name == ENT_TYPE:
         return token.ent_type
+    elif feat_name == ENT_KB_ID:
+        return token.ent_kb_id
     else:
         return Lexeme.get_struct_attr(token.lex, feat_name)
 
@@ -851,7 +853,7 @@ cdef class Doc:
 
         DOCS: https://spacy.io/api/doc#to_bytes
         """
-        array_head = [LENGTH, SPACY, LEMMA, ENT_IOB, ENT_TYPE]
+        array_head = [LENGTH, SPACY, LEMMA, ENT_IOB, ENT_TYPE]  # TODO: ENT_KB_ID ?
         if self.is_tagged:
             array_head.append(TAG)
         # If doc parsed add head and dep attribute
@@ -1005,6 +1007,7 @@ cdef class Doc:
         """
         cdef unicode tag, lemma, ent_type
         deprecation_warning(Warnings.W013.format(obj="Doc"))
+        # TODO: ENT_KB_ID ?
         if len(args) == 3:
             deprecation_warning(Warnings.W003)
             tag, lemma, ent_type = args

spacy/tokens/span.pyx

@@ -210,7 +210,7 @@ cdef class Span:
         words = [t.text for t in self]
         spaces = [bool(t.whitespace_) for t in self]
         cdef Doc doc = Doc(self.doc.vocab, words=words, spaces=spaces)
-        array_head = [LENGTH, SPACY, LEMMA, ENT_IOB, ENT_TYPE]
+        array_head = [LENGTH, SPACY, LEMMA, ENT_IOB, ENT_TYPE, ENT_KB_ID]
         if self.doc.is_tagged:
             array_head.append(TAG)
         # If doc parsed add head and dep attribute

spacy/tokens/token.pxd

@@ -53,6 +53,8 @@ cdef class Token:
             return token.ent_iob
         elif feat_name == ENT_TYPE:
             return token.ent_type
+        elif feat_name == ENT_KB_ID:
+            return token.ent_kb_id
         elif feat_name == SENT_START:
             return token.sent_start
         else:
@@ -79,5 +81,7 @@ cdef class Token:
             token.ent_iob = value
         elif feat_name == ENT_TYPE:
             token.ent_type = value
+        elif feat_name == ENT_KB_ID:
+            token.ent_kb_id = value
         elif feat_name == SENT_START:
             token.sent_start = value