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Code Issues Packages Projects Releases Wiki Activity

CLI scripts for entity linking (wikipedia & generic) (#4091)

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* document token ent_kb_id

* document span kb_id

* update pipeline documentation

* prior and context weights as bool's instead

* entitylinker api documentation

* drop for both models

* finish entitylinker documentation

* small fixes

* documentation for KB

* candidate documentation

* links to api pages in code

* small fix

* frequency examples as counts for consistency

* consistent documentation about tensors returned by predict

* add entity linking to usage 101

* add entity linking infobox and KB section to 101

* entity-linking in linguistic features

* small typo corrections

* training example and docs for entity_linker

* predefined nlp and kb

* revert back to similarity encodings for simplicity (for now)

* set prior probabilities to 0 when excluded

* code clean up

* bugfix: deleting kb ID from tokens when entities were removed

* refactor train el example to use either model or vocab

* pretrain_kb example for example kb generation

* add to training docs for KB + EL example scripts

* small fixes

* error numbering

* ensure the language of vocab and nlp stay consistent across serialization

* equality with =

* avoid conflict in errors file

* add error 151

* final adjustements to the train scripts - consistency

* update of goldparse documentation

* small corrections

* push commit

* turn kb_creator into CLI script (wip)

* proper parameters for training entity vectors

* wikidata pipeline split up into two executable scripts

* remove context_width

* move wikidata scripts in bin directory, remove old dummy script

* refine KB script with logs and preprocessing options

* small edits

* small improvements to logging of EL CLI script
This commit is contained in:
Sofie Van Landeghem 2019-08-13 15:38:59 +02:00 committed by Matthew Honnibal
parent 5196dbd89d
commit 0ba1b5eebc
18 changed files with 1111 additions and 759 deletions
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62
bin/wiki_entity_linking/kb_creator.py
View File

@ -1,16 +1,14 @@
# coding: utf-8
from __future__ import unicode_literals
from .train_descriptions import EntityEncoder
from . import wikidata_processor as wd, wikipedia_processor as wp
from bin.wiki_entity_linking.train_descriptions import EntityEncoder
from bin.wiki_entity_linking 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
from spacy import Errors
def create_kb(
@ -23,17 +21,27 @@ def create_kb(
count_input,
prior_prob_input,
wikidata_input,
entity_vector_length,
limit=None,
read_raw_data=True,
):
# Create the knowledge base from Wikidata entries
kb = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=DESC_WIDTH)
kb = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=entity_vector_length)
# check the length of the nlp vectors
if "vectors" in nlp.meta and nlp.vocab.vectors.size:
input_dim = nlp.vocab.vectors_length
print("Loaded pre-trained vectors of size %s" % input_dim)
else:
raise ValueError(Errors.E155)
# 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)
print(now(), " * read wikidata entities:")
title_to_id, id_to_descr = wd.read_wikidata_entities_json(
wikidata_input, limit=limit
)
# write the title-ID and ID-description mappings to file
_write_entity_files(
@ -46,7 +54,7 @@ def create_kb(
id_to_descr = get_id_to_description(entity_descr_output)
print()
print(" * _get_entity_frequencies", datetime.datetime.now())
print(now(), " * get entity frequencies:")
print()
entity_frequencies = wp.get_all_frequencies(count_input=count_input)
@ -65,40 +73,41 @@ def create_kb(
filtered_title_to_id[title] = entity
print(len(title_to_id.keys()), "original titles")
print("kept", len(filtered_title_to_id.keys()), " with frequency", min_entity_freq)
kept_nr = len(filtered_title_to_id.keys())
print("kept", kept_nr, "entities with min. frequency", min_entity_freq)
print()
print(" * train entity encoder", datetime.datetime.now())
print(now(), " * train entity encoder:")
print()
encoder = EntityEncoder(nlp, INPUT_DIM, DESC_WIDTH)
encoder = EntityEncoder(nlp, input_dim, entity_vector_length)
encoder.train(description_list=description_list, to_print=True)
print()
print(" * get entity embeddings", datetime.datetime.now())
print(now(), " * get entity embeddings:")
print()
embeddings = encoder.apply_encoder(description_list)
print()
print(" * adding", len(entity_list), "entities", datetime.datetime.now())
print(now(), " * adding", len(entity_list), "entities")
kb.set_entities(
entity_list=entity_list, freq_list=frequency_list, vector_list=embeddings
)
print()
print(" * adding aliases", datetime.datetime.now())
print()
_add_aliases(
alias_cnt = _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(now(), " * adding", alias_cnt, "aliases")
print()
print()
print("kb size:", len(kb), kb.get_size_entities(), kb.get_size_aliases())
print("# of entities in kb:", kb.get_size_entities())
print("# of aliases in kb:", kb.get_size_aliases())
print("done with kb", datetime.datetime.now())
print(now(), "Done with kb")
return kb
@ -140,6 +149,7 @@ def get_id_to_description(entity_descr_output):
def _add_aliases(kb, title_to_id, max_entities_per_alias, min_occ, prior_prob_input):
wp_titles = title_to_id.keys()
cnt = 0
# adding aliases with prior probabilities
# we can read this file sequentially, it's sorted by alias, and then by count
@ -176,6 +186,7 @@ def _add_aliases(kb, title_to_id, max_entities_per_alias, min_occ, prior_prob_in
entities=selected_entities,
probabilities=prior_probs,
)
cnt += 1
except ValueError as e:
print(e)
total_count = 0
@ -190,3 +201,8 @@ def _add_aliases(kb, title_to_id, max_entities_per_alias, min_occ, prior_prob_in
previous_alias = new_alias
line = prior_file.readline()
return cnt
def now():
return datetime.datetime.now()

60
bin/wiki_entity_linking/train_descriptions.py
View File

@ -18,15 +18,19 @@ class EntityEncoder:
"""
DROP = 0
EPOCHS = 5
STOP_THRESHOLD = 0.04
BATCH_SIZE = 1000
def __init__(self, nlp, input_dim, desc_width):
# Set min. acceptable loss to avoid a 'mean of empty slice' warning by numpy
MIN_LOSS = 0.01
# Reasonable default to stop training when things are not improving
MAX_NO_IMPROVEMENT = 20
def __init__(self, nlp, input_dim, desc_width, epochs=5):
self.nlp = nlp
self.input_dim = input_dim
self.desc_width = desc_width
self.epochs = epochs
def apply_encoder(self, description_list):
if self.encoder is None:
@ -46,32 +50,41 @@ class EntityEncoder:
start = start + batch_size
stop = min(stop + batch_size, len(description_list))
print("encoded:", stop, "entities")
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(
"Trained entity descriptions on",
processed,
"(non-unique) 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
best_loss = 1.0
iter_since_best = 0
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
# copy this list so that shuffling does not affect other functions
descriptions = description_list.copy()
to_continue = True
for i in range(self.EPOCHS):
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):
while to_continue and start < len(descriptions):
batch = []
for descr in descriptions[start:stop]:
doc = self.nlp(descr)
@ -79,9 +92,24 @@ class EntityEncoder:
batch.append(doc_vector)
loss = self._update(batch)
print(i, batch_nr, loss)
if batch_nr % 25 == 0:
print("loss:", loss)
processed += len(batch)
# in general, continue training if we haven't reached our ideal min yet
to_continue = loss > self.MIN_LOSS
# store the best loss and track how long it's been
if loss < best_loss:
best_loss = loss
iter_since_best = 0
else:
iter_since_best += 1
# stop learning if we haven't seen improvement since the last few iterations
if iter_since_best > self.MAX_NO_IMPROVEMENT:
to_continue = False
batch_nr += 1
start = start + self.BATCH_SIZE
stop = min(stop + self.BATCH_SIZE, len(descriptions))
@ -103,14 +131,16 @@ class EntityEncoder:
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.encoder = Affine(hidden_with, orig_width)
self.model = self.encoder >> zero_init(
Affine(orig_width, hidden_with, drop_factor=0.0)
)
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)
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)

5
bin/wiki_entity_linking/training_set_creator.py
View File

@ -21,9 +21,9 @@ def now():
return datetime.datetime.now()
def create_training(wikipedia_input, entity_def_input, training_output):
def create_training(wikipedia_input, entity_def_input, training_output, limit=None):
wp_to_id = kb_creator.get_entity_to_id(entity_def_input)
_process_wikipedia_texts(wikipedia_input, wp_to_id, training_output, limit=None)
_process_wikipedia_texts(wikipedia_input, wp_to_id, training_output, limit=limit)
def _process_wikipedia_texts(wikipedia_input, wp_to_id, training_output, limit=None):
@ -128,6 +128,7 @@ def _process_wikipedia_texts(wikipedia_input, wp_to_id, training_output, limit=N
line = file.readline()
cnt += 1
print(now(), "processed", cnt, "lines of Wikipedia dump")
text_regex = re.compile(r"(?<=<text xml:space=\"preserve\">).*(?=</text)")

139
bin/wiki_entity_linking/wikidata_pretrain_kb.py Normal file
View File

@ -0,0 +1,139 @@
# coding: utf-8
"""Script to process Wikipedia and Wikidata dumps and create a knowledge base (KB)
with specific parameters. Intermediate files are written to disk.
Running the full pipeline on a standard laptop, may take up to 13 hours of processing.
Use the -p, -d and -s options to speed up processing using the intermediate files
from a previous run.
For the Wikidata dump: get the latest-all.json.bz2 from https://dumps.wikimedia.org/wikidatawiki/entities/
For the Wikipedia dump: get enwiki-latest-pages-articles-multistream.xml.bz2
from https://dumps.wikimedia.org/enwiki/latest/
"""
from __future__ import unicode_literals
import datetime
from pathlib import Path
import plac
from bin.wiki_entity_linking import wikipedia_processor as wp
from bin.wiki_entity_linking import kb_creator
import spacy
from spacy import Errors
def now():
return datetime.datetime.now()
@plac.annotations(
wd_json=("Path to the downloaded WikiData JSON dump.", "positional", None, Path),
wp_xml=("Path to the downloaded Wikipedia XML dump.", "positional", None, Path),
output_dir=("Output directory", "positional", None, Path),
model=("Model name, should include pretrained vectors.", "positional", None, str),
max_per_alias=("Max. # entities per alias (default 10)", "option", "a", int),
min_freq=("Min. count of an entity in the corpus (default 20)", "option", "f", int),
min_pair=("Min. count of entity-alias pairs (default 5)", "option", "c", int),
entity_vector_length=("Length of entity vectors (default 64)", "option", "v", int),
loc_prior_prob=("Location to file with prior probabilities", "option", "p", Path),
loc_entity_defs=("Location to file with entity definitions", "option", "d", Path),
loc_entity_desc=("Location to file with entity descriptions", "option", "s", Path),
limit=("Optional threshold to limit lines read from dumps", "option", "l", int),
)
def main(
wd_json,
wp_xml,
output_dir,
model,
max_per_alias=10,
min_freq=20,
min_pair=5,
entity_vector_length=64,
loc_prior_prob=None,
loc_entity_defs=None,
loc_entity_desc=None,
limit=None,
):
print(now(), "Creating KB with Wikipedia and WikiData")
print()
if limit is not None:
print("Warning: reading only", limit, "lines of Wikipedia/Wikidata dumps.")
# STEP 0: set up IO
if not output_dir.exists():
output_dir.mkdir()
# STEP 1: create the NLP object
print(now(), "STEP 1: loaded model", model)
nlp = spacy.load(model)
# check the length of the nlp vectors
if "vectors" not in nlp.meta or not nlp.vocab.vectors.size:
raise ValueError(Errors.E155)
# STEP 2: create prior probabilities from WP
print()
if loc_prior_prob:
print(now(), "STEP 2: reading prior probabilities from", loc_prior_prob)
else:
# It takes about 2h to process 1000M lines of Wikipedia XML dump
loc_prior_prob = output_dir / "prior_prob.csv"
print(now(), "STEP 2: writing prior probabilities at", loc_prior_prob)
wp.read_prior_probs(wp_xml, loc_prior_prob, limit=limit)
# STEP 3: deduce entity frequencies from WP (takes only a few minutes)
print()
print(now(), "STEP 3: calculating entity frequencies")
loc_entity_freq = output_dir / "entity_freq.csv"
wp.write_entity_counts(loc_prior_prob, loc_entity_freq, to_print=False)
loc_kb = output_dir / "kb"
# STEP 4: reading entity descriptions and definitions from WikiData or from file
print()
if loc_entity_defs and loc_entity_desc:
read_raw = False
print(now(), "STEP 4a: reading entity definitions from", loc_entity_defs)
print(now(), "STEP 4b: reading entity descriptions from", loc_entity_desc)
else:
# It takes about 10h to process 55M lines of Wikidata JSON dump
read_raw = True
loc_entity_defs = output_dir / "entity_defs.csv"
loc_entity_desc = output_dir / "entity_descriptions.csv"
print(now(), "STEP 4: parsing wikidata for entity definitions and descriptions")
# STEP 5: creating the actual KB
# It takes ca. 30 minutes to pretrain the entity embeddings
print()
print(now(), "STEP 5: creating the KB at", loc_kb)
kb = kb_creator.create_kb(
nlp=nlp,
max_entities_per_alias=max_per_alias,
min_entity_freq=min_freq,
min_occ=min_pair,
entity_def_output=loc_entity_defs,
entity_descr_output=loc_entity_desc,
count_input=loc_entity_freq,
prior_prob_input=loc_prior_prob,
wikidata_input=wd_json,
entity_vector_length=entity_vector_length,
limit=limit,
read_raw_data=read_raw,
)
if read_raw:
print(" - wrote entity definitions to", loc_entity_defs)
print(" - wrote writing entity descriptions to", loc_entity_desc)
kb.dump(loc_kb)
nlp.to_disk(output_dir / "nlp")
print()
print(now(), "Done!")
if __name__ == "__main__":
plac.call(main)

52
bin/wiki_entity_linking/wikidata_processor.py
View File

@ -10,8 +10,8 @@ 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'
lang = "en"
site_filter = "enwiki"
# properties filter (currently disabled to get ALL data)
prop_filter = dict()
@ -28,12 +28,14 @@ def read_wikidata_entities_json(wikidata_file, limit=None, to_print=False):
parse_aliases = False
parse_claims = False
with bz2.open(wikidata_file, mode='rb') as file:
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")
if cnt % 1000000 == 0:
print(
datetime.datetime.now(), "processed", cnt, "lines of WikiData JSON dump"
)
clean_line = line.strip()
if clean_line.endswith(b","):
clean_line = clean_line[:-1]
@ -52,8 +54,13 @@ def read_wikidata_entities_json(wikidata_file, limit=None, to_print=False):
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']
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
@ -67,10 +74,17 @@ def read_wikidata_entities_json(wikidata_file, limit=None, to_print=False):
# 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]
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)
@ -79,7 +93,7 @@ def read_wikidata_entities_json(wikidata_file, limit=None, to_print=False):
if parse_sitelinks:
site_value = obj["sitelinks"].get(site_filter, None)
if site_value:
site = site_value['title']
site = site_value["title"]
if to_print:
print(site_filter, ":", site)
title_to_id[site] = unique_id
@ -91,7 +105,9 @@ def read_wikidata_entities_json(wikidata_file, limit=None, to_print=False):
lang_label = labels.get(lang, None)
if lang_label:
if to_print:
print("label (" + lang + "):", lang_label["value"])
print(
"label (" + lang + "):", lang_label["value"]
)
if found_link and parse_descriptions:
descriptions = obj["descriptions"]
@ -99,7 +115,10 @@ def read_wikidata_entities_json(wikidata_file, limit=None, to_print=False):
lang_descr = descriptions.get(lang, None)
if lang_descr:
if to_print:
print("description (" + lang + "):", lang_descr["value"])
print(
"description (" + lang + "):",
lang_descr["value"],
)
id_to_descr[unique_id] = lang_descr["value"]
if parse_aliases:
@ -109,11 +128,14 @@ def read_wikidata_entities_json(wikidata_file, limit=None, to_print=False):
if lang_aliases:
for item in lang_aliases:
if to_print:
print("alias (" + lang + "):", item["value"])
print(
"alias (" + lang + "):", item["value"]
)
if to_print:
print()
line = file.readline()
cnt += 1
print(datetime.datetime.now(), "processed", cnt, "lines of WikiData JSON dump")
return title_to_id, id_to_descr

430
bin/wiki_entity_linking/wikidata_train_entity_linker.py Normal file
View File

@ -0,0 +1,430 @@
# coding: utf-8
"""Script to take a previously created Knowledge Base and train an entity linking
pipeline. The provided KB directory should hold the kb, the original nlp object and
its vocab used to create the KB, and a few auxiliary files such as the entity definitions,
as created by the script `wikidata_create_kb`.
For the Wikipedia dump: get enwiki-latest-pages-articles-multistream.xml.bz2
from https://dumps.wikimedia.org/enwiki/latest/
"""
from __future__ import unicode_literals
import random
import datetime
from pathlib import Path
import plac
from bin.wiki_entity_linking import training_set_creator
import spacy
from spacy.kb import KnowledgeBase
from spacy import Errors
from spacy.util import minibatch, compounding
def now():
return datetime.datetime.now()
@plac.annotations(
dir_kb=("Directory with KB, NLP and related files", "positional", None, Path),
output_dir=("Output directory", "option", "o", Path),
loc_training=("Location to training data", "option", "k", Path),
wp_xml=("Path to the downloaded Wikipedia XML dump.", "option", "w", Path),
epochs=("Number of training iterations (default 10)", "option", "e", int),
dropout=("Dropout to prevent overfitting (default 0.5)", "option", "p", float),
lr=("Learning rate (default 0.005)", "option", "n", float),
l2=("L2 regularization", "option", "r", float),
train_inst=("# training instances (default 90% of all)", "option", "t", int),
dev_inst=("# test instances (default 10% of all)", "option", "d", int),
limit=("Optional threshold to limit lines read from WP dump", "option", "l", int),
)
def main(
dir_kb,
output_dir=None,
loc_training=None,
wp_xml=None,
epochs=10,
dropout=0.5,
lr=0.005,
l2=1e-6,
train_inst=None,
dev_inst=None,
limit=None,
):
print(now(), "Creating Entity Linker with Wikipedia and WikiData")
print()
# STEP 0: set up IO
if output_dir and not output_dir.exists():
output_dir.mkdir()
# STEP 1 : load the NLP object
nlp_dir = dir_kb / "nlp"
print(now(), "STEP 1: loading model from", nlp_dir)
nlp = spacy.load(nlp_dir)
# check that there is a NER component in the pipeline
if "ner" not in nlp.pipe_names:
raise ValueError(Errors.E152)
# STEP 2 : read the KB
print()
print(now(), "STEP 2: reading the KB from", dir_kb / "kb")
kb = KnowledgeBase(vocab=nlp.vocab)
kb.load_bulk(dir_kb / "kb")
# STEP 3: create a training dataset from WP
print()
if loc_training:
print(now(), "STEP 3: reading training dataset from", loc_training)
else:
if not wp_xml:
raise ValueError(Errors.E153)
if output_dir:
loc_training = output_dir / "training_data"
else:
loc_training = dir_kb / "training_data"
if not loc_training.exists():
loc_training.mkdir()
print(now(), "STEP 3: creating training dataset at", loc_training)
if limit is not None:
print("Warning: reading only", limit, "lines of Wikipedia dump.")
loc_entity_defs = dir_kb / "entity_defs.csv"
training_set_creator.create_training(
wikipedia_input=wp_xml,
entity_def_input=loc_entity_defs,
training_output=loc_training,
limit=limit,
)
# STEP 4: parse the training data
print()
print(now(), "STEP 4: parse the training & evaluation data")
# for training, get pos & neg instances that correspond to entries in the kb
print("Parsing training data, limit =", train_inst)
train_data = training_set_creator.read_training(
nlp=nlp, training_dir=loc_training, dev=False, limit=train_inst, kb=kb
)
print("Training on", len(train_data), "articles")
print()
print("Parsing dev testing data, limit =", dev_inst)
# for testing, get all pos instances, whether or not they are in the kb
dev_data = training_set_creator.read_training(
nlp=nlp, training_dir=loc_training, dev=True, limit=dev_inst, kb=None
)
print("Dev testing on", len(dev_data), "articles")
print()
# STEP 5: create and train the entity linking pipe
print()
print(now(), "STEP 5: training Entity Linking pipe")
el_pipe = nlp.create_pipe(
name="entity_linker", config={"pretrained_vectors": nlp.vocab.vectors.name}
)
el_pipe.set_kb(kb)
nlp.add_pipe(el_pipe, last=True)
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "entity_linker"]
with nlp.disable_pipes(*other_pipes): # only train Entity Linking
optimizer = nlp.begin_training()
optimizer.learn_rate = lr
optimizer.L2 = l2
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.disable_pipes(*other_pipes):
for batch in batches:
try:
docs, golds = zip(*batch)
nlp.update(
docs=docs,
golds=golds,
sgd=optimizer,
drop=dropout,
losses=losses,
)
batchnr += 1
except Exception as e:
print("Error updating batch:", e)
if batchnr > 0:
el_pipe.cfg["incl_context"] = True
el_pipe.cfg["incl_prior"] = True
dev_acc_context, _ = _measure_acc(dev_data, el_pipe)
losses["entity_linker"] = losses["entity_linker"] / batchnr
print(
"Epoch, train loss",
itn,
round(losses["entity_linker"], 2),
" / dev accuracy avg",
round(dev_acc_context, 3),
)
# STEP 6: measure the performance of our trained pipe on an independent dev set
print()
if len(dev_data):
print()
print(now(), "STEP 6: performance measurement of Entity Linking pipe")
print()
counts, acc_r, acc_r_d, acc_p, acc_p_d, acc_o, acc_o_d = _measure_baselines(
dev_data, kb
)
print("dev counts:", sorted(counts.items(), key=lambda x: x[0]))
oracle_by_label = [(x, round(y, 3)) for x, y in acc_o_d.items()]
print("dev accuracy oracle:", round(acc_o, 3), oracle_by_label)
random_by_label = [(x, round(y, 3)) for x, y in acc_r_d.items()]
print("dev accuracy random:", round(acc_r, 3), random_by_label)
prior_by_label = [(x, round(y, 3)) for x, y in acc_p_d.items()]
print("dev accuracy prior:", round(acc_p, 3), prior_by_label)
# using only context
el_pipe.cfg["incl_context"] = True
el_pipe.cfg["incl_prior"] = False
dev_acc_context, dev_acc_cont_d = _measure_acc(dev_data, el_pipe)
context_by_label = [(x, round(y, 3)) for x, y in dev_acc_cont_d.items()]
print("dev accuracy context:", round(dev_acc_context, 3), context_by_label)
# measuring combined accuracy (prior + context)
el_pipe.cfg["incl_context"] = True
el_pipe.cfg["incl_prior"] = True
dev_acc_combo, dev_acc_combo_d = _measure_acc(dev_data, el_pipe)
combo_by_label = [(x, round(y, 3)) for x, y in dev_acc_combo_d.items()]
print("dev accuracy prior+context:", round(dev_acc_combo, 3), combo_by_label)
# STEP 7: apply the EL pipe on a toy example
print()
print(now(), "STEP 7: applying Entity Linking to toy example")
print()
run_el_toy_example(nlp=nlp)
# STEP 8: write the NLP pipeline (including entity linker) to file
if output_dir:
print()
nlp_loc = output_dir / "nlp"
print(now(), "STEP 8: Writing trained NLP to", nlp_loc)
nlp.to_disk(nlp_loc)
print()
print()
print(now(), "Done!")
def _measure_acc(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, kb_dict in gold.links.items():
start, end = entity
# only evaluating on positive examples
for gold_kb, value in kb_dict.items():
if value:
offset = _offset(start, end)
correct_entries_per_article[offset] = gold_kb
for ent in doc.ents:
ent_label = ent.label_
pred_entity = ent.kb_id_
start = ent.start_char
end = ent.end_char
offset = _offset(start, end)
gold_entity = correct_entries_per_article.get(offset, 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_d = dict()
random_correct_d = dict()
random_incorrect_d = dict()
oracle_correct_d = dict()
oracle_incorrect_d = dict()
prior_correct_d = dict()
prior_incorrect_d = 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, kb_dict in gold.links.items():
start, end = entity
for gold_kb, value in kb_dict.items():
# only evaluating on positive examples
if value:
offset = _offset(start, end)
correct_entries_per_article[offset] = gold_kb
for ent in doc.ents:
label = ent.label_
start = ent.start_char
end = ent.end_char
offset = _offset(start, end)
gold_entity = correct_entries_per_article.get(offset, None)
# the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
if gold_entity is not None:
counts_d[label] = counts_d.get(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_d[label] = prior_correct_d.get(label, 0) + 1
else:
prior_incorrect_d[label] = prior_incorrect_d.get(label, 0) + 1
if gold_entity == random_candidate:
random_correct_d[label] = random_correct_d.get(label, 0) + 1
else:
random_incorrect_d[label] = random_incorrect_d.get(label, 0) + 1
if gold_entity == oracle_candidate:
oracle_correct_d[label] = oracle_correct_d.get(label, 0) + 1
else:
oracle_incorrect_d[label] = oracle_incorrect_d.get(label, 0) + 1
except Exception as e:
print("Error assessing accuracy", e)
acc_prior, acc_prior_d = calculate_acc(prior_correct_d, prior_incorrect_d)
acc_rand, acc_rand_d = calculate_acc(random_correct_d, random_incorrect_d)
acc_oracle, acc_oracle_d = calculate_acc(oracle_correct_d, oracle_incorrect_d)
return (
counts_d,
acc_rand,
acc_rand_d,
acc_prior,
acc_prior_d,
acc_oracle,
acc_oracle_d,
)
def _offset(start, end):
return "{}_{}".format(start, end)
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 Dougledydoug 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__":
plac.call(main)

9
bin/wiki_entity_linking/wikipedia_processor.py
View File

@ -120,7 +120,7 @@ def now():
return datetime.datetime.now()
def read_prior_probs(wikipedia_input, prior_prob_output):
def read_prior_probs(wikipedia_input, prior_prob_output, limit=None):
"""
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.
@ -129,9 +129,9 @@ def read_prior_probs(wikipedia_input, prior_prob_output):
with bz2.open(wikipedia_input, mode="rb") as file:
line = file.readline()
cnt = 0
while line:
if cnt % 5000000 == 0:
print(now(), "processed", cnt, "lines of Wikipedia dump")
while line and (not limit or cnt < limit):
if cnt % 25000000 == 0:
print(now(), "processed", cnt, "lines of Wikipedia XML dump")
clean_line = line.strip().decode("utf-8")
aliases, entities, normalizations = get_wp_links(clean_line)
@ -141,6 +141,7 @@ def read_prior_probs(wikipedia_input, prior_prob_output):
line = file.readline()
cnt += 1
print(now(), "processed", cnt, "lines of Wikipedia XML dump")
# write all aliases and their entities and count occurrences to file
with prior_prob_output.open("w", encoding="utf8") as outputfile:

75
examples/pipeline/dummy_entity_linking.py
View File

@ -1,75 +0,0 @@
# coding: utf-8
from __future__ import unicode_literals
"""Demonstrate how to build a simple knowledge base and run an Entity Linking algorithm.
Currently still a bit of a dummy algorithm: taking simply the entity with highest probability for a given alias
"""
import spacy
from spacy.kb import KnowledgeBase
def create_kb(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, freq=0.5, entity_vector=[0])
entity_1 = "Q42_Douglas_Adams"
print("adding entity", entity_1)
kb.add_entity(entity=entity_1, freq=0.5, entity_vector=[1])
entity_2 = "Q5301561_Douglas_Haig"
print("adding entity", entity_2)
kb.add_entity(entity=entity_2, freq=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=[0.6, 0.1, 0.2])
alias_1 = "Douglas Adams"
print("adding alias", alias_1)
kb.add_alias(alias=alias_1, entities=[entity_1], probabilities=[0.9])
print()
print("kb size:", len(kb), kb.get_size_entities(), kb.get_size_aliases())
return kb
def add_el(kb, nlp):
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)
print()
print(len(candidates), "candidate(s) for", alias, ":")
for c in candidates:
print(" ", c.entity_, c.prior_prob)
text = "In The Hitchhiker's Guide to the Galaxy, written by Douglas Adams, " \
"Douglas reminds us to always bring our towel. " \
"The main character in Doug's novel is called Arthur Dent."
doc = nlp(text)
print()
for token in doc:
print("token", token.text, token.ent_type_, token.ent_kb_id_)
print()
for ent in doc.ents:
print("ent", ent.text, ent.label_, ent.kb_id_)
if __name__ == "__main__":
my_nlp = spacy.load('en_core_web_sm')
my_kb = create_kb(my_nlp.vocab)
add_el(my_kb, my_nlp)

514
examples/pipeline/wikidata_entity_linking.py
View File

@ -1,514 +0,0 @@
# coding: utf-8
from __future__ import unicode_literals
import os
from os import path
import random
import datetime
from pathlib import Path
from bin.wiki_entity_linking import wikipedia_processor as wp
from bin.wiki_entity_linking import training_set_creator, kb_creator
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_DIR = OUTPUT_DIR / "kb_1"
KB_FILE = "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 now():
return datetime.datetime.now()
def run_pipeline():
# set the appropriate booleans to define which parts of the pipeline should be re(run)
print("START", 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", now())
wp.read_prior_probs(ENWIKI_DUMP, 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", now())
wp.write_entity_counts(PRIOR_PROB, 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", now())
kb_1 = kb_creator.create_kb(
nlp=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", now())
if not path.exists(KB_DIR):
os.makedirs(KB_DIR)
kb_1.dump(KB_DIR / 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", 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_DIR / 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", 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", 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
# for training, get pos & neg instances that correspond to entries in the kb
train_data = training_set_creator.read_training(
nlp=nlp_2,
training_dir=TRAINING_DIR,
dev=False,
limit=train_limit,
kb=el_pipe.kb,
)
print("Training on", len(train_data), "articles")
print()
# for testing, get all pos instances, whether or not they are in the kb
dev_data = training_set_creator.read_training(
nlp=nlp_2, training_dir=TRAINING_DIR, dev=True, limit=dev_limit, kb=None
)
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=docs,
golds=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, _ = _measure_acc(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", now())
print()
counts, acc_r, acc_r_d, acc_p, acc_p_d, acc_o, acc_o_d = _measure_baselines(
dev_data, kb_2
)
print("dev counts:", sorted(counts.items(), key=lambda x: x[0]))
oracle_by_label = [(x, round(y, 3)) for x, y in acc_o_d.items()]
print("dev acc oracle:", round(acc_o, 3), oracle_by_label)
random_by_label = [(x, round(y, 3)) for x, y in acc_r_d.items()]
print("dev acc random:", round(acc_r, 3), random_by_label)
prior_by_label = [(x, round(y, 3)) for x, y in acc_p_d.items()]
print("dev acc prior:", round(acc_p, 3), prior_by_label)
# using only context
el_pipe.cfg["context_weight"] = 1
el_pipe.cfg["prior_weight"] = 0
dev_acc_context, dev_acc_cont_d = _measure_acc(dev_data, el_pipe)
context_by_label = [(x, round(y, 3)) for x, y in dev_acc_cont_d.items()]
print("dev acc context avg:", round(dev_acc_context, 3), context_by_label)
# measuring combined accuracy (prior + context)
el_pipe.cfg["context_weight"] = 1
el_pipe.cfg["prior_weight"] = 1
dev_acc_combo, dev_acc_combo_d = _measure_acc(dev_data, el_pipe)
combo_by_label = [(x, round(y, 3)) for x, y in dev_acc_combo_d.items()]
print("dev acc combo avg:", round(dev_acc_combo, 3), combo_by_label)
# STEP 8: apply the EL pipe on a toy example
if to_test_pipeline:
print()
print("STEP 8: applying Entity Linking to toy example", 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", 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, kb=None
)
print("Dev testing from file on", len(dev_data), "articles")
print()
dev_acc_combo, dev_acc_combo_dict = _measure_acc(dev_data, el_pipe)
combo_by_label = [(x, round(y, 3)) for x, y in dev_acc_combo_dict.items()]
print("dev acc combo avg:", round(dev_acc_combo, 3), combo_by_label)
print()
print("STOP", now())
def _measure_acc(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, kb_dict in gold.links.items():
start, end = entity
# only evaluating on positive examples
for gold_kb, value in kb_dict.items():
if value:
offset = _offset(start, end)
correct_entries_per_article[offset] = gold_kb
for ent in doc.ents:
ent_label = ent.label_
pred_entity = ent.kb_id_
start = ent.start_char
end = ent.end_char
offset = _offset(start, end)
gold_entity = correct_entries_per_article.get(offset, 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_d = dict()
random_correct_d = dict()
random_incorrect_d = dict()
oracle_correct_d = dict()
oracle_incorrect_d = dict()
prior_correct_d = dict()
prior_incorrect_d = 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, kb_dict in gold.links.items():
start, end = entity
for gold_kb, value in kb_dict.items():
# only evaluating on positive examples
if value:
offset = _offset(start, end)
correct_entries_per_article[offset] = gold_kb
for ent in doc.ents:
label = ent.label_
start = ent.start_char
end = ent.end_char
offset = _offset(start, end)
gold_entity = correct_entries_per_article.get(offset, None)
# the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
if gold_entity is not None:
counts_d[label] = counts_d.get(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_d[label] = prior_correct_d.get(label, 0) + 1
else:
prior_incorrect_d[label] = prior_incorrect_d.get(label, 0) + 1
if gold_entity == random_candidate:
random_correct_d[label] = random_correct_d.get(label, 0) + 1
else:
random_incorrect_d[label] = random_incorrect_d.get(label, 0) + 1
if gold_entity == oracle_candidate:
oracle_correct_d[label] = oracle_correct_d.get(label, 0) + 1
else:
oracle_incorrect_d[label] = oracle_incorrect_d.get(label, 0) + 1
except Exception as e:
print("Error assessing accuracy", e)
acc_prior, acc_prior_d = calculate_acc(prior_correct_d, prior_incorrect_d)
acc_rand, acc_rand_d = calculate_acc(random_correct_d, random_incorrect_d)
acc_oracle, acc_oracle_d = calculate_acc(oracle_correct_d, oracle_incorrect_d)
return (
counts_d,
acc_rand,
acc_rand_d,
acc_prior,
acc_prior_d,
acc_oracle,
acc_oracle_d,
)
def _offset(start, end):
return "{}_{}".format(start, end)
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 Dougledydoug 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()

139
examples/training/pretrain_kb.py Normal file
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@ -0,0 +1,139 @@
#!/usr/bin/env python
# coding: utf8
"""Example of defining and (pre)training spaCy's knowledge base,
which is needed to implement entity linking functionality.
For more details, see the documentation:
* Knowledge base: https://spacy.io/api/kb
* Entity Linking: https://spacy.io/usage/linguistic-features#entity-linking
Compatible with: spaCy vX.X
Last tested with: vX.X
"""
from __future__ import unicode_literals, print_function
import plac
from pathlib import Path
from spacy.vocab import Vocab
import spacy
from spacy.kb import KnowledgeBase
from bin.wiki_entity_linking.train_descriptions import EntityEncoder
from spacy import Errors
# Q2146908 (Russ Cochran): American golfer
# Q7381115 (Russ Cochran): publisher
ENTITIES = {"Q2146908": ("American golfer", 342), "Q7381115": ("publisher", 17)}
INPUT_DIM = 300 # dimension of pre-trained input vectors
DESC_WIDTH = 64 # dimension of output entity vectors
@plac.annotations(
vocab_path=("Path to the vocab for the kb", "option", "v", Path),
model=("Model name, should have pretrained word embeddings", "option", "m", str),
output_dir=("Optional output directory", "option", "o", Path),
n_iter=("Number of training iterations", "option", "n", int),
)
def main(vocab_path=None, model=None, output_dir=None, n_iter=50):
"""Load the model, create the KB and pretrain the entity encodings.
Either an nlp model or a vocab is needed to provide access to pre-trained word embeddings.
If an output_dir is provided, the KB will be stored there in a file 'kb'.
When providing an nlp model, the updated vocab will also be written to a directory in the output_dir."""
if model is None and vocab_path is None:
raise ValueError(Errors.E154)
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
vocab = Vocab().from_disk(vocab_path)
# create blank Language class with specified vocab
nlp = spacy.blank("en", vocab=vocab)
print("Created blank 'en' model with vocab from '%s'" % vocab_path)
kb = KnowledgeBase(vocab=nlp.vocab)
# set up the data
entity_ids = []
descriptions = []
freqs = []
for key, value in ENTITIES.items():
desc, freq = value
entity_ids.append(key)
descriptions.append(desc)
freqs.append(freq)
# training entity description encodings
# this part can easily be replaced with a custom entity encoder
encoder = EntityEncoder(
nlp=nlp,
input_dim=INPUT_DIM,
desc_width=DESC_WIDTH,
epochs=n_iter,
threshold=0.001,
)
encoder.train(description_list=descriptions, to_print=True)
# get the pretrained entity vectors
embeddings = encoder.apply_encoder(descriptions)
# set the entities, can also be done by calling `kb.add_entity` for each entity
kb.set_entities(entity_list=entity_ids, freq_list=freqs, vector_list=embeddings)
# adding aliases, the entities need to be defined in the KB beforehand
kb.add_alias(
alias="Russ Cochran",
entities=["Q2146908", "Q7381115"],
probabilities=[0.24, 0.7], # the sum of these probabilities should not exceed 1
)
# test the trained model
print()
_print_kb(kb)
# save model to output directory
if output_dir is not None:
output_dir = Path(output_dir)
if not output_dir.exists():
output_dir.mkdir()
kb_path = str(output_dir / "kb")
kb.dump(kb_path)
print()
print("Saved KB to", kb_path)
# only storing the vocab if we weren't already reading it from file
if not vocab_path:
vocab_path = output_dir / "vocab"
kb.vocab.to_disk(vocab_path)
print("Saved vocab to", vocab_path)
print()
# test the saved model
# always reload a knowledge base with the same vocab instance!
print("Loading vocab from", vocab_path)
print("Loading KB from", kb_path)
vocab2 = Vocab().from_disk(vocab_path)
kb2 = KnowledgeBase(vocab=vocab2)
kb2.load_bulk(kb_path)
_print_kb(kb2)
print()
def _print_kb(kb):
print(kb.get_size_entities(), "kb entities:", kb.get_entity_strings())
print(kb.get_size_aliases(), "kb aliases:", kb.get_alias_strings())
if __name__ == "__main__":
plac.call(main)
# Expected output:
# 2 kb entities: ['Q2146908', 'Q7381115']
# 1 kb aliases: ['Russ Cochran']

173
examples/training/train_entity_linker.py Normal file
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@ -0,0 +1,173 @@
#!/usr/bin/env python
# coding: utf8
"""Example of training spaCy's entity linker, starting off with an
existing model and a pre-defined knowledge base.
For more details, see the documentation:
* Training: https://spacy.io/usage/training
* Entity Linking: https://spacy.io/usage/linguistic-features#entity-linking
Compatible with: spaCy vX.X
Last tested with: vX.X
"""
from __future__ import unicode_literals, print_function
import plac
import random
from pathlib import Path
from spacy.symbols import PERSON
from spacy.vocab import Vocab
import spacy
from spacy.kb import KnowledgeBase
from spacy import Errors
from spacy.tokens import Span
from spacy.util import minibatch, compounding
def sample_train_data():
train_data = []
# Q2146908 (Russ Cochran): American golfer
# Q7381115 (Russ Cochran): publisher
text_1 = "Russ Cochran his reprints include EC Comics."
dict_1 = {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}
train_data.append((text_1, {"links": dict_1}))
text_2 = "Russ Cochran has been publishing comic art."
dict_2 = {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}
train_data.append((text_2, {"links": dict_2}))
text_3 = "Russ Cochran captured his first major title with his son as caddie."
dict_3 = {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}}
train_data.append((text_3, {"links": dict_3}))
text_4 = "Russ Cochran was a member of University of Kentucky's golf team."
dict_4 = {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}}
train_data.append((text_4, {"links": dict_4}))
return train_data
# training data
TRAIN_DATA = sample_train_data()
@plac.annotations(
kb_path=("Path to the knowledge base", "positional", None, Path),
vocab_path=("Path to the vocab for the kb", "positional", None, Path),
output_dir=("Optional output directory", "option", "o", Path),
n_iter=("Number of training iterations", "option", "n", int),
)
def main(kb_path, vocab_path=None, output_dir=None, n_iter=50):
"""Create a blank model with the specified vocab, set up the pipeline and train the entity linker.
The `vocab` should be the one used during creation of the KB."""
vocab = Vocab().from_disk(vocab_path)
# create blank Language class with correct vocab
nlp = spacy.blank("en", vocab=vocab)
nlp.vocab.vectors.name = "spacy_pretrained_vectors"
print("Created blank 'en' model with vocab from '%s'" % vocab_path)
# create the built-in pipeline components and add them to the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
if "entity_linker" not in nlp.pipe_names:
entity_linker = nlp.create_pipe("entity_linker")
kb = KnowledgeBase(vocab=nlp.vocab)
kb.load_bulk(kb_path)
print("Loaded Knowledge Base from '%s'" % kb_path)
entity_linker.set_kb(kb)
nlp.add_pipe(entity_linker, last=True)
else:
entity_linker = nlp.get_pipe("entity_linker")
kb = entity_linker.kb
# make sure the annotated examples correspond to known identifiers in the knowlege base
kb_ids = kb.get_entity_strings()
for text, annotation in TRAIN_DATA:
for offset, kb_id_dict in annotation["links"].items():
new_dict = {}
for kb_id, value in kb_id_dict.items():
if kb_id in kb_ids:
new_dict[kb_id] = value
else:
print(
"Removed", kb_id, "from training because it is not in the KB."
)
annotation["links"][offset] = new_dict
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "entity_linker"]
with nlp.disable_pipes(*other_pipes): # only train entity linker
# reset and initialize the weights randomly
optimizer = nlp.begin_training()
for itn in range(n_iter):
random.shuffle(TRAIN_DATA)
losses = {}
# batch up the examples using spaCy's minibatch
batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
texts, annotations = zip(*batch)
nlp.update(
texts, # batch of texts
annotations, # batch of annotations
drop=0.2, # dropout - make it harder to memorise data
losses=losses,
sgd=optimizer,
)
print(itn, "Losses", losses)
# test the trained model
_apply_model(nlp)
# save model to output directory
if output_dir is not None:
output_dir = Path(output_dir)
if not output_dir.exists():
output_dir.mkdir()
nlp.to_disk(output_dir)
print()
print("Saved model to", output_dir)
# test the saved model
print("Loading from", output_dir)
nlp2 = spacy.load(output_dir)
_apply_model(nlp2)
def _apply_model(nlp):
for text, annotation in TRAIN_DATA:
doc = nlp.tokenizer(text)
# set entities so the evaluation is independent of the NER step
# all the examples contain 'Russ Cochran' as the first two tokens in the sentence
rc_ent = Span(doc, 0, 2, label=PERSON)
doc.ents = [rc_ent]
# apply the entity linker which will now make predictions for the 'Russ Cochran' entities
doc = nlp.get_pipe("entity_linker")(doc)
print()
print("Entities", [(ent.text, ent.label_, ent.kb_id_) for ent in doc.ents])
print("Tokens", [(t.text, t.ent_type_, t.ent_kb_id_) for t in doc])
if __name__ == "__main__":
plac.call(main)
# Expected output (can be shuffled):
# Entities[('Russ Cochran', 'PERSON', 'Q7381115')]
# Tokens[('Russ', 'PERSON', 'Q7381115'), ('Cochran', 'PERSON', 'Q7381115'), ("his", '', ''), ('reprints', '', ''), ('include', '', ''), ('The', '', ''), ('Complete', '', ''), ('EC', '', ''), ('Library', '', ''), ('.', '', '')]
# Entities[('Russ Cochran', 'PERSON', 'Q7381115')]
# Tokens[('Russ', 'PERSON', 'Q7381115'), ('Cochran', 'PERSON', 'Q7381115'), ('has', '', ''), ('been', '', ''), ('publishing', '', ''), ('comic', '', ''), ('art', '', ''), ('.', '', '')]
# Entities[('Russ Cochran', 'PERSON', 'Q2146908')]
# Tokens[('Russ', 'PERSON', 'Q2146908'), ('Cochran', 'PERSON', 'Q2146908'), ('captured', '', ''), ('his', '', ''), ('first', '', ''), ('major', '', ''), ('title', '', ''), ('with', '', ''), ('his', '', ''), ('son', '', ''), ('as', '', ''), ('caddie', '', ''), ('.', '', '')]
# Entities[('Russ Cochran', 'PERSON', 'Q2146908')]
# Tokens[('Russ', 'PERSON', 'Q2146908'), ('Cochran', 'PERSON', 'Q2146908'), ('was', '', ''), ('a', '', ''), ('member', '', ''), ('of', '', ''), ('University', '', ''), ('of', '', ''), ('Kentucky', '', ''), ("'s", '', ''), ('golf', '', ''), ('team', '', ''), ('.', '', '')]

24
spacy/_ml.py
View File

@ -665,25 +665,15 @@ def build_simple_cnn_text_classifier(tok2vec, nr_class, exclusive_classes=False,
def build_nel_encoder(embed_width, hidden_width, ner_types, **cfg):
if "entity_width" not in cfg:
raise ValueError(Errors.E144.format(param="entity_width"))
if "context_width" not in cfg:
raise ValueError(Errors.E144.format(param="context_width"))
conv_depth = cfg.get("conv_depth", 2)
cnn_maxout_pieces = cfg.get("cnn_maxout_pieces", 3)
pretrained_vectors = cfg.get("pretrained_vectors", None)
context_width = cfg.get("context_width")
entity_width = cfg.get("entity_width")
context_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(
tok2vec = Tok2Vec(
width=hidden_width,
embed_size=embed_width,
pretrained_vectors=pretrained_vectors,
@ -692,17 +682,17 @@ def build_nel_encoder(embed_width, hidden_width, ner_types, **cfg):
conv_depth=conv_depth,
bilstm_depth=0,
)
model = (
tok2vec
>> flatten_add_lengths
>> Pooling(mean_pool)
>> Residual(zero_init(Maxout(hidden_width, hidden_width)))
>> zero_init(Affine(context_width, hidden_width))
>> zero_init(Affine(context_width, hidden_width, drop_factor=0.0))
)
model.tok2vec = tok2vec
model.tok2vec = tok2vec
model.tok2vec.nO = context_width
model.nO = 1
model.nO = context_width
return model

10
spacy/errors.py
View File

@ -124,7 +124,8 @@ class Errors(object):
E016 = ("MultitaskObjective target should be function or one of: dep, "
"tag, ent, dep_tag_offset, ent_tag.")
E017 = ("Can only add unicode or bytes. Got type: {value_type}")
E018 = ("Can't retrieve string for hash '{hash_value}'.")
E018 = ("Can't retrieve string for hash '{hash_value}'. This usually refers "
"to an issue with the `Vocab` or `StringStore`.")
E019 = ("Can't create transition with unknown action ID: {action}. Action "
"IDs are enumerated in spacy/syntax/{src}.pyx.")
E020 = ("Could not find a gold-standard action to supervise the "
@ -420,7 +421,12 @@ class Errors(object):
E151 = ("Trying to call nlp.update without required annotation types. "
"Expected top-level keys: {expected_keys}."
" Got: {unexpected_keys}.")
E152 = ("The `nlp` object should have a pre-trained `ner` component.")
E153 = ("Either provide a path to a preprocessed training directory, "
"or to the original Wikipedia XML dump.")
E154 = ("Either the `nlp` model or the `vocab` should be specified.")
E155 = ("The `nlp` object should have access to pre-trained word vectors, cf. "
"https://spacy.io/usage/models#languages.")
@add_codes
class TempErrors(object):

14
spacy/kb.pyx
View File

@ -19,6 +19,13 @@ from libcpp.vector cimport vector
cdef class Candidate:
"""A `Candidate` object refers to a textual mention (`alias`) that may or may not be resolved
to a specific `entity` from a Knowledge Base. This will be used as input for the entity linking
algorithm which will disambiguate the various candidates to the correct one.
Each candidate (alias, entity) pair is assigned to a certain prior probability.
DOCS: https://spacy.io/api/candidate
"""
def __init__(self, KnowledgeBase kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob):
self.kb = kb
@ -62,8 +69,13 @@ cdef class Candidate:
cdef class KnowledgeBase:
"""A `KnowledgeBase` instance stores unique identifiers for entities and their textual aliases,
to support entity linking of named entities to real-world concepts.
def __init__(self, Vocab vocab, entity_vector_length):
DOCS: https://spacy.io/api/kb
"""
def __init__(self, Vocab vocab, entity_vector_length=64):
self.vocab = vocab
self.mem = Pool()
self.entity_vector_length = entity_vector_length

105
spacy/pipeline/pipes.pyx
View File

@ -14,6 +14,8 @@ from thinc.neural.util import to_categorical
from thinc.neural.util import get_array_module
from spacy.kb import KnowledgeBase
from spacy.cli.pretrain import get_cossim_loss
from .functions import merge_subtokens
from ..tokens.doc cimport Doc
from ..syntax.nn_parser cimport Parser
@ -1102,7 +1104,7 @@ cdef class EntityRecognizer(Parser):
class EntityLinker(Pipe):
"""Pipeline component for named entity linking.
DOCS: TODO
DOCS: https://spacy.io/api/entitylinker
"""
name = 'entity_linker'
NIL = "NIL" # string used to refer to a non-existing link
@ -1121,9 +1123,6 @@ class EntityLinker(Pipe):
self.model = True
self.kb = None
self.cfg = dict(cfg)
self.sgd_context = None
if not self.cfg.get("context_width"):
self.cfg["context_width"] = 128
def set_kb(self, kb):
self.kb = kb
@ -1144,7 +1143,6 @@ class EntityLinker(Pipe):
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()
@ -1170,12 +1168,6 @@ class EntityLinker(Pipe):
golds = [golds]
context_docs = []
entity_encodings = []
priors = []
type_vectors = []
type_to_int = self.cfg.get("type_to_int", dict())
for doc, gold in zip(docs, golds):
ents_by_offset = dict()
@ -1184,49 +1176,38 @@ class EntityLinker(Pipe):
for entity, kb_dict in gold.links.items():
start, end = entity
mention = doc.text[start:end]
for kb_id, value in kb_dict.items():
entity_encoding = self.kb.get_vector(kb_id)
prior_prob = self.kb.get_prior_prob(kb_id, mention)
# Currently only training on the positive instances
if value:
context_docs.append(doc)
gold_ent = ents_by_offset["{}_{}".format(start, end)]
if gold_ent is None:
raise RuntimeError(Errors.E147.format(method="update", msg="gold entity not found"))
context_encodings, bp_context = self.model.begin_update(context_docs, drop=drop)
loss, d_scores = self.get_similarity_loss(scores=context_encodings, golds=golds, docs=None)
bp_context(d_scores, sgd=sgd)
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
if losses is not None:
losses[self.name] += loss
return loss
# store data
entity_encodings.append(entity_encoding)
context_docs.append(doc)
type_vectors.append(type_vector)
def get_similarity_loss(self, docs, golds, scores):
entity_encodings = []
for gold in golds:
for entity, kb_dict in gold.links.items():
for kb_id, value in kb_dict.items():
# this loss function assumes we're only using positive examples
if value:
entity_encoding = self.kb.get_vector(kb_id)
entity_encodings.append(entity_encoding)
if self.cfg.get("prior_weight", 1) > 0:
priors.append([prior_prob])
else:
priors.append([0])
entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32")
if len(entity_encodings) > 0:
if not (len(priors) == len(entity_encodings) == len(context_docs) == len(type_vectors)):
raise RuntimeError(Errors.E147.format(method="update", msg="vector lengths not equal"))
if scores.shape != entity_encodings.shape:
raise RuntimeError(Errors.E147.format(method="get_loss", msg="gold entities do not match up"))
entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32")
context_encodings, bp_context = self.model.tok2vec.begin_update(context_docs, drop=drop)
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)
loss, d_scores = self.get_loss(scores=pred, golds=golds, docs=docs)
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
loss, gradients = get_cossim_loss(yh=scores, y=entity_encodings)
loss = loss / len(entity_encodings)
return loss, gradients
def get_loss(self, docs, golds, scores):
cats = []
@ -1271,20 +1252,17 @@ class EntityLinker(Pipe):
if isinstance(docs, Doc):
docs = [docs]
context_encodings = self.model.tok2vec(docs)
context_encodings = self.model(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:
# currently, the context is the same for each entity in a sentence (should be refined)
context_encoding = context_encodings[i]
context_enc_t = context_encoding.T
norm_1 = xp.linalg.norm(context_enc_t)
for ent in doc.ents:
entity_count += 1
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 not candidates:
@ -1293,20 +1271,23 @@ class EntityLinker(Pipe):
else:
random.shuffle(candidates)
# this will set the prior probabilities to 0 (just like in training) if their weight is 0
prior_probs = xp.asarray([[c.prior_prob] for c in candidates])
prior_probs *= self.cfg.get("prior_weight", 1)
# this will set all prior probabilities to 0 if they should be excluded from the model
prior_probs = xp.asarray([c.prior_prob for c in candidates])
if not self.cfg.get("incl_prior", True):
prior_probs = xp.asarray([[0.0] for c in candidates])
scores = prior_probs
if self.cfg.get("context_weight", 1) > 0:
# add in similarity from the context
if self.cfg.get("incl_context", True):
entity_encodings = xp.asarray([c.entity_vector for c in candidates])
norm_2 = xp.linalg.norm(entity_encodings, axis=1)
if len(entity_encodings) != len(prior_probs):
raise RuntimeError(Errors.E147.format(method="predict", msg="vectors not of equal length"))
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"))
# cosine similarity
sims = xp.dot(entity_encodings, context_enc_t) / (norm_1 * norm_2)
scores = prior_probs + sims - (prior_probs*sims)
# TODO: thresholding
best_index = scores.argmax()
@ -1346,7 +1327,7 @@ class EntityLinker(Pipe):
def load_model(p):
if self.model is True:
self.model = self.Model(**self.cfg)
try:
try:
self.model.from_bytes(p.open("rb").read())
except AttributeError:
raise ValueError(Errors.E149)

46
spacy/tests/pipeline/test_entity_linker.py
View File

@ -23,9 +23,9 @@ def test_kb_valid_entities(nlp):
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
# adding entities
mykb.add_entity(entity="Q1", freq=0.9, entity_vector=[8, 4, 3])
mykb.add_entity(entity="Q2", freq=0.5, entity_vector=[2, 1, 0])
mykb.add_entity(entity="Q3", freq=0.5, entity_vector=[-1, -6, 5])
mykb.add_entity(entity="Q1", freq=19, entity_vector=[8, 4, 3])
mykb.add_entity(entity="Q2", freq=5, entity_vector=[2, 1, 0])
mykb.add_entity(entity="Q3", freq=25, entity_vector=[-1, -6, 5])
# adding aliases
mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.2])
@ -52,9 +52,9 @@ def test_kb_invalid_entities(nlp):
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=0.9, entity_vector=[1])
mykb.add_entity(entity="Q2", freq=0.2, entity_vector=[2])
mykb.add_entity(entity="Q3", freq=0.5, entity_vector=[3])
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
mykb.add_entity(entity="Q2", freq=5, entity_vector=[2])
mykb.add_entity(entity="Q3", freq=25, entity_vector=[3])
# adding aliases - should fail because one of the given IDs is not valid
with pytest.raises(ValueError):
@ -68,9 +68,9 @@ def test_kb_invalid_probabilities(nlp):
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=0.9, entity_vector=[1])
mykb.add_entity(entity="Q2", freq=0.2, entity_vector=[2])
mykb.add_entity(entity="Q3", freq=0.5, entity_vector=[3])
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
mykb.add_entity(entity="Q2", freq=5, entity_vector=[2])
mykb.add_entity(entity="Q3", freq=25, entity_vector=[3])
# adding aliases - should fail because the sum of the probabilities exceeds 1
with pytest.raises(ValueError):
@ -82,9 +82,9 @@ def test_kb_invalid_combination(nlp):
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=0.9, entity_vector=[1])
mykb.add_entity(entity="Q2", freq=0.2, entity_vector=[2])
mykb.add_entity(entity="Q3", freq=0.5, entity_vector=[3])
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
mykb.add_entity(entity="Q2", freq=5, entity_vector=[2])
mykb.add_entity(entity="Q3", freq=25, entity_vector=[3])
# adding aliases - should fail because the entities and probabilities vectors are not of equal length
with pytest.raises(ValueError):
@ -98,11 +98,11 @@ def test_kb_invalid_entity_vector(nlp):
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
# adding entities
mykb.add_entity(entity="Q1", freq=0.9, entity_vector=[1, 2, 3])
mykb.add_entity(entity="Q1", freq=19, 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", freq=0.2, entity_vector=[2])
mykb.add_entity(entity="Q2", freq=5, entity_vector=[2])
def test_candidate_generation(nlp):
@ -110,9 +110,9 @@ def test_candidate_generation(nlp):
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=0.7, entity_vector=[1])
mykb.add_entity(entity="Q2", freq=0.2, entity_vector=[2])
mykb.add_entity(entity="Q3", freq=0.5, entity_vector=[3])
mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
mykb.add_entity(entity="Q2", freq=12, entity_vector=[2])
mykb.add_entity(entity="Q3", freq=5, entity_vector=[3])
# adding aliases
mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.1])
@ -126,7 +126,7 @@ def test_candidate_generation(nlp):
# test the content of the candidates
assert mykb.get_candidates("adam")[0].entity_ == "Q2"
assert mykb.get_candidates("adam")[0].alias_ == "adam"
assert_almost_equal(mykb.get_candidates("adam")[0].entity_freq, 0.2)
assert_almost_equal(mykb.get_candidates("adam")[0].entity_freq, 12)
assert_almost_equal(mykb.get_candidates("adam")[0].prior_prob, 0.9)
@ -135,8 +135,8 @@ def test_preserving_links_asdoc(nlp):
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=0.9, entity_vector=[1])
mykb.add_entity(entity="Q2", freq=0.8, entity_vector=[1])
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
mykb.add_entity(entity="Q2", freq=8, entity_vector=[1])
# adding aliases
mykb.add_alias(alias="Boston", entities=["Q1"], probabilities=[0.7])
@ -154,11 +154,11 @@ def test_preserving_links_asdoc(nlp):
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)
el_pipe = nlp.create_pipe(name="entity_linker", config={"context_width": 64})
el_pipe = nlp.create_pipe(name="entity_linker")
el_pipe.set_kb(mykb)
el_pipe.begin_training()
el_pipe.context_weight = 0
el_pipe.prior_weight = 1
el_pipe.incl_context = False
el_pipe.incl_prior = True
nlp.add_pipe(el_pipe, last=True)
# test whether the entity links are preserved by the `as_doc()` function

12
spacy/tests/serialize/test_serialize_kb.py
View File

@ -30,10 +30,10 @@ def test_serialize_kb_disk(en_vocab):
def _get_dummy_kb(vocab):
kb = KnowledgeBase(vocab=vocab, entity_vector_length=3)
kb.add_entity(entity='Q53', freq=0.33, entity_vector=[0, 5, 3])
kb.add_entity(entity='Q17', freq=0.2, entity_vector=[7, 1, 0])
kb.add_entity(entity='Q007', freq=0.7, entity_vector=[0, 0, 7])
kb.add_entity(entity='Q44', freq=0.4, entity_vector=[4, 4, 4])
kb.add_entity(entity='Q53', freq=33, entity_vector=[0, 5, 3])
kb.add_entity(entity='Q17', freq=2, entity_vector=[7, 1, 0])
kb.add_entity(entity='Q007', freq=7, entity_vector=[0, 0, 7])
kb.add_entity(entity='Q44', freq=342, 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])
@ -62,13 +62,13 @@ def _check_kb(kb):
assert len(candidates) == 2
assert candidates[0].entity_ == 'Q007'
assert 0.6999 < candidates[0].entity_freq < 0.701
assert 6.999 < candidates[0].entity_freq < 7.01
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 1.99 < candidates[1].entity_freq < 2.01
assert candidates[1].entity_vector == [7, 1, 0]
assert candidates[1].alias_ == 'double07'
assert 0.099 < candidates[1].prior_prob < 0.101

1
spacy/tokens/doc.pyx
View File

@ -546,6 +546,7 @@ cdef class Doc:
cdef int i
for i in range(self.length):
self.c[i].ent_type = 0
self.c[i].ent_kb_id = 0
self.c[i].ent_iob = 0 # Means missing.
cdef attr_t ent_type
cdef int start, end