mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 01:46:28 +03:00
creating training data with clean WP texts and QID entities true/false
This commit is contained in:
svlandeg
parent
cba9680d13
commit
bbcb9da466
|
|
@ -29,6 +29,8 @@ ENTITY_DEFS = 'C:/Users/Sofie/Documents/data/wikipedia/entity_defs.csv'
|
|||
KB_FILE = 'C:/Users/Sofie/Documents/data/wikipedia/kb'
|
||||
VOCAB_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/vocab'
|
||||
|
||||
TRAINING_SET_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/training_nel/'
|
||||
|
||||
|
||||
# these will/should be matched ignoring case
|
||||
wiki_namespaces = ["b", "betawikiversity", "Book", "c", "Category", "Commons",
|
||||
|
|
@ -224,7 +226,7 @@ def create_kb(vocab, max_entities_per_alias, min_occ, to_print=False, write_enti
|
|||
print()
|
||||
print("4. adding aliases", datetime.datetime.now())
|
||||
print()
|
||||
_add_aliases(kb, title_to_id=title_to_id, max_entities_per_alias=max_entities_per_alias, min_occ=min_occ,)
|
||||
_add_aliases(kb, title_to_id=title_to_id, max_entities_per_alias=max_entities_per_alias, min_occ=min_occ)
|
||||
|
||||
# TODO: read wikipedia texts for entity context
|
||||
# _read_wikipedia()
|
||||
|
|
@ -512,18 +514,27 @@ def add_coref():
|
|||
print(doc._.coref_clusters)
|
||||
|
||||
|
||||
def create_training():
|
||||
nlp = spacy.load('en_core_web_sm')
|
||||
def create_training(kb):
|
||||
if not kb:
|
||||
raise ValueError("kb should be defined")
|
||||
# nlp = spacy.load('en_core_web_sm')
|
||||
wp_to_id = _get_entity_to_id()
|
||||
_read_wikipedia_texts(nlp, wp_to_id, limit=10000)
|
||||
_read_wikipedia_texts(kb, wp_to_id, limit=None)
|
||||
|
||||
|
||||
def _read_wikipedia_texts(nlp, wp_to_id, limit=None):
|
||||
def _read_wikipedia_texts(kb, wp_to_id, limit=None):
|
||||
""" Read the XML wikipedia data to parse out training data """
|
||||
|
||||
title_regex = re.compile(r'(?<=<title>).*(?=</title>)')
|
||||
id_regex = re.compile(r'(?<=<id>)\d*(?=</id>)')
|
||||
|
||||
# read entity training header file
|
||||
_write_training_entity(article_id="article_id",
|
||||
alias="alias",
|
||||
entity="entity",
|
||||
correct="correct",
|
||||
append=False)
|
||||
|
||||
with bz2.open(ENWIKI_DUMP, mode='rb') as file:
|
||||
line = file.readline()
|
||||
cnt = 1
|
||||
|
|
@ -532,6 +543,8 @@ def _read_wikipedia_texts(nlp, wp_to_id, limit=None):
|
|||
article_id = None
|
||||
reading_text = False
|
||||
while line and (not limit or cnt < limit):
|
||||
if cnt % 500000 == 0:
|
||||
print(datetime.datetime.now(), "processed", cnt, "lines of Wikipedia dump")
|
||||
clean_line = line.strip().decode("utf-8")
|
||||
|
||||
# Start reading new page
|
||||
|
|
@ -543,7 +556,7 @@ def _read_wikipedia_texts(nlp, wp_to_id, limit=None):
|
|||
# finished reading this page
|
||||
elif clean_line == "</page>":
|
||||
if article_id:
|
||||
_process_wp_text(nlp, wp_to_id, article_id, article_title, article_text.strip())
|
||||
_process_wp_text(kb, wp_to_id, article_id, article_title, article_text.strip())
|
||||
|
||||
# start reading text within a page
|
||||
if "<text" in clean_line:
|
||||
|
|
@ -570,7 +583,7 @@ def _read_wikipedia_texts(nlp, wp_to_id, limit=None):
|
|||
cnt += 1
|
||||
|
||||
|
||||
def _process_wp_text(nlp, wp_to_id, article_id, article_title, article_text):
|
||||
def _process_wp_text(kb, wp_to_id, article_id, article_title, article_text):
|
||||
# remove the text tags
|
||||
text_regex = re.compile(r'(?<=<text xml:space=\"preserve\">).*(?=</text>)')
|
||||
text = text_regex.search(article_text).group(0)
|
||||
|
|
@ -579,7 +592,14 @@ def _process_wp_text(nlp, wp_to_id, article_id, article_title, article_text):
|
|||
if text.startswith("#REDIRECT"):
|
||||
return
|
||||
|
||||
print("WP article", article_id, ":", article_title)
|
||||
# print("WP article", article_id, ":", article_title)
|
||||
# print()
|
||||
# print(text)
|
||||
|
||||
# get the raw text without markup etc
|
||||
clean_text = _get_clean_wp_text(text)
|
||||
# print()
|
||||
# print(clean_text)
|
||||
|
||||
article_dict = dict()
|
||||
aliases, entities, normalizations = _get_wp_links(text)
|
||||
|
|
@ -589,12 +609,37 @@ def _process_wp_text(nlp, wp_to_id, article_id, article_title, article_text):
|
|||
article_dict[alias] = entity_id
|
||||
article_dict[entity] = entity_id
|
||||
|
||||
# get the raw text without markup etc
|
||||
clean_text = _get_clean_wp_text(text)
|
||||
print(clean_text)
|
||||
# print("found entities:")
|
||||
for alias, entity in article_dict.items():
|
||||
# print(alias, "-->", entity)
|
||||
candidates = kb.get_candidates(alias)
|
||||
|
||||
_run_ner(nlp, article_id, article_title, clean_text, article_dict)
|
||||
print()
|
||||
# as training data, we only store entities that are sufficiently ambiguous
|
||||
if len(candidates) > 1:
|
||||
_write_training_article(article_id=article_id, clean_text=clean_text)
|
||||
# print("alias", alias)
|
||||
|
||||
# print all incorrect candidates
|
||||
for c in candidates:
|
||||
if entity != c.entity_:
|
||||
_write_training_entity(article_id=article_id,
|
||||
alias=alias,
|
||||
entity=c.entity_,
|
||||
correct="0",
|
||||
append=True)
|
||||
|
||||
# print the one correct candidate
|
||||
_write_training_entity(article_id=article_id,
|
||||
alias=alias,
|
||||
entity=entity,
|
||||
correct="1",
|
||||
append=True)
|
||||
|
||||
# print("gold entity", entity)
|
||||
# print()
|
||||
|
||||
# _run_ner_depr(nlp, article_id, article_title, clean_text, article_dict)
|
||||
# print()
|
||||
|
||||
|
||||
info_regex = re.compile(r'{[^{]*?}')
|
||||
|
|
@ -669,7 +714,22 @@ def _get_clean_wp_text(article_text):
|
|||
return clean_text.strip()
|
||||
|
||||
|
||||
def _run_ner(nlp, article_id, article_title, clean_text, article_dict):
|
||||
def _write_training_article(article_id, clean_text):
|
||||
file_loc = TRAINING_SET_DIR + "/" + str(article_id) + ".txt"
|
||||
with open(file_loc, mode='w', encoding='utf8') as outputfile:
|
||||
outputfile.write(clean_text)
|
||||
|
||||
|
||||
def _write_training_entity(article_id, alias, entity, correct, append=True):
|
||||
mode = "w"
|
||||
if append:
|
||||
mode = "a"
|
||||
file_loc = TRAINING_SET_DIR + "/" + "gold_entities.csv"
|
||||
with open(file_loc, mode=mode, encoding='utf8') as outputfile:
|
||||
outputfile.write(article_id + "|" + alias + "|" + entity + "|" + correct + "\n")
|
||||
|
||||
|
||||
def _run_ner_depr(nlp, article_id, article_title, clean_text, article_dict):
|
||||
doc = nlp(clean_text)
|
||||
for ent in doc.ents:
|
||||
if ent.label_ == "PERSON": # TODO: expand to non-persons
|
||||
|
|
@ -691,7 +751,7 @@ if __name__ == "__main__":
|
|||
to_create_kb = False
|
||||
|
||||
# read KB back in from file
|
||||
to_read_kb = False
|
||||
to_read_kb = True
|
||||
to_test_kb = False
|
||||
|
||||
create_wp_training = True
|
||||
|
|
@ -745,7 +805,7 @@ if __name__ == "__main__":
|
|||
# STEP 5: create a training dataset from WP
|
||||
if create_wp_training:
|
||||
print("STEP 5: create training dataset", datetime.datetime.now())
|
||||
create_training()
|
||||
create_training(my_kb)
|
||||
|
||||
# TODO coreference resolution
|
||||
# add_coref()
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user