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creating training data with clean WP texts and QID entities true/false
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svlandeg
parent
cba9680d13
commit
bbcb9da466
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@ -29,6 +29,8 @@ ENTITY_DEFS = 'C:/Users/Sofie/Documents/data/wikipedia/entity_defs.csv'
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KB_FILE = 'C:/Users/Sofie/Documents/data/wikipedia/kb'
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VOCAB_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/vocab'
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TRAINING_SET_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/training_nel/'
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# these will/should be matched ignoring case
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wiki_namespaces = ["b", "betawikiversity", "Book", "c", "Category", "Commons",
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@ -224,7 +226,7 @@ def create_kb(vocab, max_entities_per_alias, min_occ, to_print=False, write_enti
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print()
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print("4. adding aliases", datetime.datetime.now())
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print()
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_add_aliases(kb, title_to_id=title_to_id, max_entities_per_alias=max_entities_per_alias, min_occ=min_occ,)
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_add_aliases(kb, title_to_id=title_to_id, max_entities_per_alias=max_entities_per_alias, min_occ=min_occ)
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# TODO: read wikipedia texts for entity context
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# _read_wikipedia()
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@ -512,18 +514,27 @@ def add_coref():
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print(doc._.coref_clusters)
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def create_training():
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nlp = spacy.load('en_core_web_sm')
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def create_training(kb):
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if not kb:
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raise ValueError("kb should be defined")
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# nlp = spacy.load('en_core_web_sm')
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wp_to_id = _get_entity_to_id()
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_read_wikipedia_texts(nlp, wp_to_id, limit=10000)
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_read_wikipedia_texts(kb, wp_to_id, limit=None)
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def _read_wikipedia_texts(nlp, wp_to_id, limit=None):
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def _read_wikipedia_texts(kb, wp_to_id, limit=None):
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""" Read the XML wikipedia data to parse out training data """
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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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# read entity training header file
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_write_training_entity(article_id="article_id",
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alias="alias",
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entity="entity",
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correct="correct",
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append=False)
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with bz2.open(ENWIKI_DUMP, mode='rb') as file:
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line = file.readline()
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cnt = 1
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@ -532,6 +543,8 @@ def _read_wikipedia_texts(nlp, wp_to_id, limit=None):
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article_id = None
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reading_text = False
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while line and (not limit or cnt < limit):
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if cnt % 500000 == 0:
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print(datetime.datetime.now(), "processed", cnt, "lines of Wikipedia dump")
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clean_line = line.strip().decode("utf-8")
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# Start reading new page
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@ -543,7 +556,7 @@ def _read_wikipedia_texts(nlp, wp_to_id, limit=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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_process_wp_text(nlp, wp_to_id, article_id, article_title, article_text.strip())
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_process_wp_text(kb, wp_to_id, article_id, article_title, article_text.strip())
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# start reading text within a page
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if "<text" in clean_line:
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@ -570,7 +583,7 @@ def _read_wikipedia_texts(nlp, wp_to_id, limit=None):
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cnt += 1
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def _process_wp_text(nlp, wp_to_id, article_id, article_title, article_text):
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def _process_wp_text(kb, wp_to_id, article_id, article_title, article_text):
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# remove the text tags
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text_regex = re.compile(r'(?<=<text xml:space=\"preserve\">).*(?=</text>)')
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text = text_regex.search(article_text).group(0)
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@ -579,7 +592,14 @@ def _process_wp_text(nlp, wp_to_id, article_id, article_title, article_text):
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if text.startswith("#REDIRECT"):
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return
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print("WP article", article_id, ":", article_title)
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# print("WP article", article_id, ":", article_title)
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# print()
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# print(text)
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# get the raw text without markup etc
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clean_text = _get_clean_wp_text(text)
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# print()
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# print(clean_text)
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article_dict = dict()
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aliases, entities, normalizations = _get_wp_links(text)
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@ -589,12 +609,37 @@ def _process_wp_text(nlp, wp_to_id, article_id, article_title, article_text):
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article_dict[alias] = entity_id
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article_dict[entity] = entity_id
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# get the raw text without markup etc
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clean_text = _get_clean_wp_text(text)
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print(clean_text)
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# print("found entities:")
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for alias, entity in article_dict.items():
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# print(alias, "-->", entity)
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candidates = kb.get_candidates(alias)
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_run_ner(nlp, article_id, article_title, clean_text, article_dict)
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print()
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# as training data, we only store entities that are sufficiently ambiguous
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if len(candidates) > 1:
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_write_training_article(article_id=article_id, clean_text=clean_text)
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# print("alias", alias)
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# print all incorrect candidates
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for c in candidates:
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if entity != c.entity_:
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_write_training_entity(article_id=article_id,
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alias=alias,
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entity=c.entity_,
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correct="0",
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append=True)
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# print the one correct candidate
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_write_training_entity(article_id=article_id,
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alias=alias,
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entity=entity,
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correct="1",
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append=True)
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# print("gold entity", entity)
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# print()
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# _run_ner_depr(nlp, article_id, article_title, clean_text, article_dict)
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# print()
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info_regex = re.compile(r'{[^{]*?}')
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@ -669,7 +714,22 @@ def _get_clean_wp_text(article_text):
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return clean_text.strip()
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def _run_ner(nlp, article_id, article_title, clean_text, article_dict):
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def _write_training_article(article_id, clean_text):
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file_loc = TRAINING_SET_DIR + "/" + str(article_id) + ".txt"
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with open(file_loc, mode='w', encoding='utf8') as outputfile:
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outputfile.write(clean_text)
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def _write_training_entity(article_id, alias, entity, correct, append=True):
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mode = "w"
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if append:
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mode = "a"
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file_loc = TRAINING_SET_DIR + "/" + "gold_entities.csv"
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with open(file_loc, mode=mode, encoding='utf8') as outputfile:
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outputfile.write(article_id + "|" + alias + "|" + entity + "|" + correct + "\n")
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def _run_ner_depr(nlp, article_id, article_title, clean_text, article_dict):
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doc = nlp(clean_text)
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for ent in doc.ents:
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if ent.label_ == "PERSON": # TODO: expand to non-persons
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@ -691,7 +751,7 @@ if __name__ == "__main__":
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to_create_kb = False
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# read KB back in from file
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to_read_kb = False
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to_read_kb = True
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to_test_kb = False
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create_wp_training = True
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@ -745,7 +805,7 @@ if __name__ == "__main__":
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# STEP 5: create a training dataset from WP
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if create_wp_training:
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print("STEP 5: create training dataset", datetime.datetime.now())
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create_training()
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create_training(my_kb)
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# TODO coreference resolution
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# add_coref()
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