mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-13 13:17:06 +03:00
2d249a9502
* fix overflow error on windows * more documentation & logging fixes * md fix * 3 different limit parameters to play with execution time * bug fixes directory locations * small fixes * exclude dev test articles from prior probabilities stats * small fixes * filtering wikidata entities, removing numeric and meta items * adding aliases from wikidata also to the KB * fix adding WD aliases * adding also new aliases to previously added entities * fixing comma's * small doc fixes * adding subclassof filtering * append alias functionality in KB * prevent appending the same entity-alias pair * fix for appending WD aliases * remove date filter * remove unnecessary import * small corrections and reformatting * remove WD aliases for now (too slow) * removing numeric entities from training and evaluation * small fixes * shortcut during prediction if there is only one candidate * add counts and fscore logging, remove FP NER from evaluation * fix entity_linker.predict to take docs instead of single sentences * remove enumeration sentences from the WP dataset * entity_linker.update to process full doc instead of single sentence * spelling corrections and dump locations in readme * NLP IO fix * reading KB is unnecessary at the end of the pipeline * small logging fix * remove empty files
557 lines
19 KiB
Python
557 lines
19 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import re
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import bz2
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import logging
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import random
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import json
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from functools import partial
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from spacy.gold import GoldParse
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from bin.wiki_entity_linking import wiki_io as io
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from bin.wiki_entity_linking.wiki_namespaces import (
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WP_META_NAMESPACE,
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WP_FILE_NAMESPACE,
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WP_CATEGORY_NAMESPACE,
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)
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"""
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Process a Wikipedia dump to calculate entity frequencies and prior probabilities in combination with certain mentions.
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Write these results to file for downstream KB and training data generation.
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Process Wikipedia interlinks to generate a training dataset for the EL algorithm.
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"""
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ENTITY_FILE = "gold_entities.csv"
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map_alias_to_link = dict()
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logger = logging.getLogger(__name__)
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title_regex = re.compile(r"(?<=<title>).*(?=</title>)")
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id_regex = re.compile(r"(?<=<id>)\d*(?=</id>)")
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text_regex = re.compile(r"(?<=<text xml:space=\"preserve\">).*(?=</text)")
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info_regex = re.compile(r"{[^{]*?}")
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html_regex = re.compile(r"<!--[^-]*-->")
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ref_regex = re.compile(r"<ref.*?>") # non-greedy
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ref_2_regex = re.compile(r"</ref.*?>") # non-greedy
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# find the links
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link_regex = re.compile(r"\[\[[^\[\]]*\]\]")
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# match on interwiki links, e.g. `en:` or `:fr:`
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ns_regex = r":?" + "[a-z][a-z]" + ":"
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# match on Namespace: optionally preceded by a :
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for ns in WP_META_NAMESPACE:
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ns_regex += "|" + ":?" + ns + ":"
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ns_regex = re.compile(ns_regex, re.IGNORECASE)
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files = r""
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for f in WP_FILE_NAMESPACE:
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files += "\[\[" + f + ":[^[\]]+]]" + "|"
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files = files[0 : len(files) - 1]
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file_regex = re.compile(files)
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cats = r""
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for c in WP_CATEGORY_NAMESPACE:
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cats += "\[\[" + c + ":[^\[]*]]" + "|"
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cats = cats[0 : len(cats) - 1]
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category_regex = re.compile(cats)
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def read_prior_probs(wikipedia_input, prior_prob_output, limit=None):
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"""
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Read the XML wikipedia data and parse out intra-wiki links to estimate prior probabilities.
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The full file takes about 2-3h to parse 1100M lines.
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It works relatively fast because it runs line by line, irrelevant of which article the intrawiki is from,
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though dev test articles are excluded in order not to get an artificially strong baseline.
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"""
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cnt = 0
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read_id = False
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current_article_id = None
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with bz2.open(wikipedia_input, mode="rb") as file:
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line = file.readline()
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while line and (not limit or cnt < limit):
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if cnt % 25000000 == 0 and cnt > 0:
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logger.info("processed {} lines of Wikipedia XML dump".format(cnt))
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clean_line = line.strip().decode("utf-8")
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# we attempt at reading the article's ID (but not the revision or contributor ID)
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if "<revision>" in clean_line or "<contributor>" in clean_line:
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read_id = False
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if "<page>" in clean_line:
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read_id = True
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if read_id:
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ids = id_regex.search(clean_line)
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if ids:
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current_article_id = ids[0]
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# only processing prior probabilities from true training (non-dev) articles
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if not is_dev(current_article_id):
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aliases, entities, normalizations = get_wp_links(clean_line)
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for alias, entity, norm in zip(aliases, entities, normalizations):
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_store_alias(
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alias, entity, normalize_alias=norm, normalize_entity=True
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)
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line = file.readline()
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cnt += 1
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logger.info("processed {} lines of Wikipedia XML dump".format(cnt))
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logger.info("Finished. processed {} lines of Wikipedia XML dump".format(cnt))
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# write all aliases and their entities and count occurrences to file
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with prior_prob_output.open("w", encoding="utf8") as outputfile:
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outputfile.write("alias" + "|" + "count" + "|" + "entity" + "\n")
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for alias, alias_dict in sorted(map_alias_to_link.items(), key=lambda x: x[0]):
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s_dict = sorted(alias_dict.items(), key=lambda x: x[1], reverse=True)
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for entity, count in s_dict:
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outputfile.write(alias + "|" + str(count) + "|" + entity + "\n")
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def _store_alias(alias, entity, normalize_alias=False, normalize_entity=True):
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alias = alias.strip()
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entity = entity.strip()
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# remove everything after # as this is not part of the title but refers to a specific paragraph
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if normalize_entity:
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# wikipedia titles are always capitalized
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entity = _capitalize_first(entity.split("#")[0])
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if normalize_alias:
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alias = alias.split("#")[0]
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if alias and entity:
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alias_dict = map_alias_to_link.get(alias, dict())
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entity_count = alias_dict.get(entity, 0)
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alias_dict[entity] = entity_count + 1
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map_alias_to_link[alias] = alias_dict
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def get_wp_links(text):
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aliases = []
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entities = []
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normalizations = []
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matches = link_regex.findall(text)
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for match in matches:
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match = match[2:][:-2].replace("_", " ").strip()
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if ns_regex.match(match):
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pass # ignore the entity if it points to a "meta" page
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# this is a simple [[link]], with the alias the same as the mention
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elif "|" not in match:
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aliases.append(match)
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entities.append(match)
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normalizations.append(True)
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# in wiki format, the link is written as [[entity|alias]]
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else:
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splits = match.split("|")
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entity = splits[0].strip()
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alias = splits[1].strip()
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# specific wiki format [[alias (specification)|]]
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if len(alias) == 0 and "(" in entity:
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alias = entity.split("(")[0]
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aliases.append(alias)
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entities.append(entity)
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normalizations.append(False)
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else:
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aliases.append(alias)
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entities.append(entity)
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normalizations.append(False)
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return aliases, entities, normalizations
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def _capitalize_first(text):
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if not text:
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return None
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result = text[0].capitalize()
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if len(result) > 0:
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result += text[1:]
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return result
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def create_training_and_desc(
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wp_input, def_input, desc_output, training_output, parse_desc, limit=None
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):
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wp_to_id = io.read_title_to_id(def_input)
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_process_wikipedia_texts(
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wp_input, wp_to_id, desc_output, training_output, parse_desc, limit
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)
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def _process_wikipedia_texts(
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wikipedia_input, wp_to_id, output, training_output, parse_descriptions, limit=None
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):
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"""
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Read the XML wikipedia data to parse out training data:
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raw text data + positive instances
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"""
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read_ids = set()
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with output.open("a", encoding="utf8") as descr_file, training_output.open(
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"w", encoding="utf8"
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) as entity_file:
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if parse_descriptions:
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_write_training_description(descr_file, "WD_id", "description")
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with bz2.open(wikipedia_input, mode="rb") as file:
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article_count = 0
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article_text = ""
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article_title = None
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article_id = None
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reading_text = False
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reading_revision = False
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for line in file:
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clean_line = line.strip().decode("utf-8")
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if clean_line == "<revision>":
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reading_revision = True
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elif clean_line == "</revision>":
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reading_revision = False
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# Start reading new page
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if clean_line == "<page>":
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article_text = ""
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article_title = None
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article_id = None
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# finished reading this page
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elif clean_line == "</page>":
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if article_id:
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clean_text, entities = _process_wp_text(
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article_title, article_text, wp_to_id
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)
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if clean_text is not None and entities is not None:
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_write_training_entities(
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entity_file, article_id, clean_text, entities
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)
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if article_title in wp_to_id and parse_descriptions:
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description = " ".join(
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clean_text[:1000].split(" ")[:-1]
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)
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_write_training_description(
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descr_file, wp_to_id[article_title], description
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)
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article_count += 1
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if article_count % 10000 == 0 and article_count > 0:
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logger.info(
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"Processed {} articles".format(article_count)
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)
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if limit and article_count >= limit:
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break
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article_text = ""
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article_title = None
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article_id = None
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reading_text = False
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reading_revision = False
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# start reading text within a page
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if "<text" in clean_line:
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reading_text = True
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if reading_text:
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article_text += " " + clean_line
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# stop reading text within a page (we assume a new page doesn't start on the same line)
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if "</text" in clean_line:
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reading_text = False
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# read the ID of this article (outside the revision portion of the document)
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if not reading_revision:
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ids = id_regex.search(clean_line)
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if ids:
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article_id = ids[0]
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if article_id in read_ids:
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logger.info(
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"Found duplicate article ID", article_id, clean_line
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) # This should never happen ...
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read_ids.add(article_id)
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# read the title of this article (outside the revision portion of the document)
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if not reading_revision:
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titles = title_regex.search(clean_line)
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if titles:
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article_title = titles[0].strip()
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logger.info("Finished. Processed {} articles".format(article_count))
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def _process_wp_text(article_title, article_text, wp_to_id):
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# ignore meta Wikipedia pages
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if ns_regex.match(article_title):
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return None, None
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# remove the text tags
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text_search = text_regex.search(article_text)
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if text_search is None:
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return None, None
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text = text_search.group(0)
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# stop processing if this is a redirect page
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if text.startswith("#REDIRECT"):
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return None, None
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# get the raw text without markup etc, keeping only interwiki links
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clean_text, entities = _remove_links(_get_clean_wp_text(text), wp_to_id)
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return clean_text, entities
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def _get_clean_wp_text(article_text):
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clean_text = article_text.strip()
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# remove bolding & italic markup
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clean_text = clean_text.replace("'''", "")
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clean_text = clean_text.replace("''", "")
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# remove nested {{info}} statements by removing the inner/smallest ones first and iterating
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try_again = True
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previous_length = len(clean_text)
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while try_again:
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clean_text = info_regex.sub(
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"", clean_text
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) # non-greedy match excluding a nested {
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if len(clean_text) < previous_length:
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try_again = True
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else:
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try_again = False
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previous_length = len(clean_text)
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# remove HTML comments
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clean_text = html_regex.sub("", clean_text)
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# remove Category and File statements
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clean_text = category_regex.sub("", clean_text)
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clean_text = file_regex.sub("", clean_text)
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# remove multiple =
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while "==" in clean_text:
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clean_text = clean_text.replace("==", "=")
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clean_text = clean_text.replace(". =", ".")
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clean_text = clean_text.replace(" = ", ". ")
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clean_text = clean_text.replace("= ", ".")
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clean_text = clean_text.replace(" =", "")
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# remove refs (non-greedy match)
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clean_text = ref_regex.sub("", clean_text)
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clean_text = ref_2_regex.sub("", clean_text)
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# remove additional wikiformatting
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clean_text = re.sub(r"<blockquote>", "", clean_text)
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clean_text = re.sub(r"</blockquote>", "", clean_text)
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# change special characters back to normal ones
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clean_text = clean_text.replace(r"<", "<")
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clean_text = clean_text.replace(r">", ">")
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clean_text = clean_text.replace(r""", '"')
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clean_text = clean_text.replace(r"&nbsp;", " ")
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clean_text = clean_text.replace(r"&", "&")
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# remove multiple spaces
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while " " in clean_text:
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clean_text = clean_text.replace(" ", " ")
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return clean_text.strip()
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def _remove_links(clean_text, wp_to_id):
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# read the text char by char to get the right offsets for the interwiki links
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entities = []
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final_text = ""
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open_read = 0
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reading_text = True
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reading_entity = False
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reading_mention = False
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reading_special_case = False
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entity_buffer = ""
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mention_buffer = ""
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for index, letter in enumerate(clean_text):
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if letter == "[":
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open_read += 1
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elif letter == "]":
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open_read -= 1
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elif letter == "|":
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if reading_text:
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final_text += letter
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# switch from reading entity to mention in the [[entity|mention]] pattern
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elif reading_entity:
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reading_text = False
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reading_entity = False
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reading_mention = True
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else:
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reading_special_case = True
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else:
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if reading_entity:
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entity_buffer += letter
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elif reading_mention:
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mention_buffer += letter
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elif reading_text:
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final_text += letter
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else:
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raise ValueError("Not sure at point", clean_text[index - 2 : index + 2])
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if open_read > 2:
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reading_special_case = True
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if open_read == 2 and reading_text:
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reading_text = False
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reading_entity = True
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reading_mention = False
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# we just finished reading an entity
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if open_read == 0 and not reading_text:
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if "#" in entity_buffer or entity_buffer.startswith(":"):
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reading_special_case = True
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# Ignore cases with nested structures like File: handles etc
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if not reading_special_case:
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if not mention_buffer:
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mention_buffer = entity_buffer
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start = len(final_text)
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end = start + len(mention_buffer)
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qid = wp_to_id.get(entity_buffer, None)
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if qid:
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entities.append((mention_buffer, qid, start, end))
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final_text += mention_buffer
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entity_buffer = ""
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mention_buffer = ""
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reading_text = True
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reading_entity = False
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reading_mention = False
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reading_special_case = False
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return final_text, entities
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def _write_training_description(outputfile, qid, description):
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if description is not None:
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line = str(qid) + "|" + description + "\n"
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outputfile.write(line)
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def _write_training_entities(outputfile, article_id, clean_text, entities):
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entities_data = [
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{"alias": ent[0], "entity": ent[1], "start": ent[2], "end": ent[3]}
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for ent in entities
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]
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line = (
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json.dumps(
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{
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"article_id": article_id,
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"clean_text": clean_text,
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"entities": entities_data,
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},
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ensure_ascii=False,
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)
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+ "\n"
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)
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outputfile.write(line)
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def read_training(nlp, entity_file_path, dev, limit, kb, labels_discard=None):
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""" This method provides training examples that correspond to the entity annotations found by the nlp object.
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For training, it will include both positive and negative examples by using the candidate generator from the kb.
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For testing (kb=None), it will include all positive examples only."""
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from tqdm import tqdm
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if not labels_discard:
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labels_discard = []
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data = []
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num_entities = 0
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get_gold_parse = partial(
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_get_gold_parse, dev=dev, kb=kb, labels_discard=labels_discard
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)
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logger.info(
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"Reading {} data with limit {}".format("dev" if dev else "train", limit)
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)
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with entity_file_path.open("r", encoding="utf8") as file:
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with tqdm(total=limit, leave=False) as pbar:
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for i, line in enumerate(file):
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example = json.loads(line)
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article_id = example["article_id"]
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clean_text = example["clean_text"]
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entities = example["entities"]
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if dev != is_dev(article_id) or not is_valid_article(clean_text):
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continue
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doc = nlp(clean_text)
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gold = get_gold_parse(doc, entities)
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if gold and len(gold.links) > 0:
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data.append((doc, gold))
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num_entities += len(gold.links)
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pbar.update(len(gold.links))
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if limit and num_entities >= limit:
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break
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logger.info("Read {} entities in {} articles".format(num_entities, len(data)))
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return data
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def _get_gold_parse(doc, entities, dev, kb, labels_discard):
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gold_entities = {}
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tagged_ent_positions = {
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(ent.start_char, ent.end_char): ent
|
|
for ent in doc.ents
|
|
if ent.label_ not in labels_discard
|
|
}
|
|
|
|
for entity in entities:
|
|
entity_id = entity["entity"]
|
|
alias = entity["alias"]
|
|
start = entity["start"]
|
|
end = entity["end"]
|
|
|
|
candidate_ids = []
|
|
if kb and not dev:
|
|
candidates = kb.get_candidates(alias)
|
|
candidate_ids = [cand.entity_ for cand in candidates]
|
|
|
|
tagged_ent = tagged_ent_positions.get((start, end), None)
|
|
if tagged_ent:
|
|
# TODO: check that alias == doc.text[start:end]
|
|
should_add_ent = (dev or entity_id in candidate_ids) and is_valid_sentence(
|
|
tagged_ent.sent.text
|
|
)
|
|
|
|
if should_add_ent:
|
|
value_by_id = {entity_id: 1.0}
|
|
if not dev:
|
|
random.shuffle(candidate_ids)
|
|
value_by_id.update(
|
|
{kb_id: 0.0 for kb_id in candidate_ids if kb_id != entity_id}
|
|
)
|
|
gold_entities[(start, end)] = value_by_id
|
|
|
|
return GoldParse(doc, links=gold_entities)
|
|
|
|
|
|
def is_dev(article_id):
|
|
if not article_id:
|
|
return False
|
|
return article_id.endswith("3")
|
|
|
|
|
|
def is_valid_article(doc_text):
|
|
# custom length cut-off
|
|
return 10 < len(doc_text) < 30000
|
|
|
|
|
|
def is_valid_sentence(sent_text):
|
|
if not 10 < len(sent_text) < 3000:
|
|
# custom length cut-off
|
|
return False
|
|
|
|
if sent_text.strip().startswith("*") or sent_text.strip().startswith("#"):
|
|
# remove 'enumeration' sentences (occurs often on Wikipedia)
|
|
return False
|
|
|
|
return True
|