explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2025-03-31 07:14:13 +03:00
Code Issues Packages Projects Releases Wiki Activity

obtain sentence for each mention

Browse Source
This commit is contained in:
svlandeg 2019-05-23 15:37:05 +02:00
parent 97241a3ed7
commit 4392c01b7b
3 changed files with 112 additions and 43 deletions
examples/pipeline/wiki_entity_linking
run_el.pytrain_el.pywiki_nel_pipeline.py

9
examples/pipeline/wiki_entity_linking/run_el.py
View File

@ -70,7 +70,7 @@ def is_dev(file_name):
return file_name.endswith("3.txt")
def evaluate(predictions, golds, to_print=True):
def evaluate(predictions, golds, to_print=True, times_hundred=True):
if len(predictions) != len(golds):
raise ValueError("predictions and gold entities should have the same length")
@ -101,8 +101,11 @@ def evaluate(predictions, golds, to_print=True):
print("fp", fp)
print("fn", fn)
precision = 100 * tp / (tp + fp + 0.0000001)
recall = 100 * tp / (tp + fn + 0.0000001)
precision = tp / (tp + fp + 0.0000001)
recall = tp / (tp + fn + 0.0000001)
if times_hundred:
precision = precision*100
recall = recall*100
fscore = 2 * recall * precision / (recall + precision + 0.0000001)
accuracy = corrects / (corrects + incorrects)

144
examples/pipeline/wiki_entity_linking/train_el.py
View File

@ -20,6 +20,7 @@ from thinc.t2t import ParametricAttention
from thinc.misc import Residual
from thinc.misc import LayerNorm as LN
from spacy.matcher import PhraseMatcher
from spacy.tokens import Doc
""" TODO: this code needs to be implemented in pipes.pyx"""
@ -27,13 +28,16 @@ from spacy.tokens import Doc
class EL_Model:
PRINT_INSPECT = False
PRINT_TRAIN = False
EPS = 0.0000000005
CUTOFF = 0.5
BATCH_SIZE = 5
INPUT_DIM = 300
DOC_CUTOFF = 300 # number of characters from the doc context
INPUT_DIM = 300 # dimension of pre-trained vectors
HIDDEN_1_WIDTH = 32 # 10
HIDDEN_2_WIDTH = 32 # 6
DESC_WIDTH = 64 # 4
@ -58,11 +62,20 @@ class EL_Model:
# raise errors instead of runtime warnings in case of int/float overflow
np.seterr(all='raise')
train_ent, train_gold, train_desc, train_article, train_texts = self._get_training_data(training_dir,
entity_descr_output,
False,
trainlimit,
to_print=False)
train_ent, train_gold, train_desc, train_art, train_art_texts, train_sent, train_sent_texts = \
self._get_training_data(training_dir, entity_descr_output, False, trainlimit, to_print=False)
# inspect data
if self.PRINT_INSPECT:
for entity in train_ent:
print("entity", entity)
print("gold", train_gold[entity])
print("desc", train_desc[entity])
print("sentence ID", train_sent[entity])
print("sentence text", train_sent_texts[train_sent[entity]])
print("article ID", train_art[entity])
print("article text", train_art_texts[train_art[entity]])
print()
train_pos_entities = [k for k,v in train_gold.items() if v]
train_neg_entities = [k for k,v in train_gold.items() if not v]
@ -70,6 +83,10 @@ class EL_Model:
train_pos_count = len(train_pos_entities)
train_neg_count = len(train_neg_entities)
if to_print:
print()
print("Upsampling, original training instances pos/neg:", train_pos_count, train_neg_count)
# upsample positives to 50-50 distribution
while train_pos_count < train_neg_count:
train_ent.append(random.choice(train_pos_entities))
@ -82,11 +99,8 @@ class EL_Model:
shuffle(train_ent)
dev_ent, dev_gold, dev_desc, dev_article, dev_texts = self._get_training_data(training_dir,
entity_descr_output,
True,
devlimit,
to_print=False)
dev_ent, dev_gold, dev_desc, dev_art, dev_art_texts, dev_sent, dev_sent_texts = \
self._get_training_data(training_dir, entity_descr_output, True, devlimit, to_print=False)
shuffle(dev_ent)
dev_pos_count = len([g for g in dev_gold.values() if g])
@ -94,20 +108,16 @@ class EL_Model:
self._begin_training()
print()
self._test_dev(dev_ent, dev_gold, dev_desc, dev_article, dev_texts, print_string="dev_random", calc_random=True)
print()
self._test_dev(dev_ent, dev_gold, dev_desc, dev_article, dev_texts, print_string="dev_pre", avg=True)
if to_print:
print()
print("Training on", len(train_ent), "entities in", len(train_texts), "articles")
print("Training instances pos/neg", train_pos_count, train_neg_count)
print("Training on", len(train_ent), "entities in", len(train_art_texts), "articles")
print("Training instances pos/neg:", train_pos_count, train_neg_count)
print()
print("Dev test on", len(dev_ent), "entities in", len(dev_texts), "articles")
print("Dev instances pos/neg", dev_pos_count, dev_neg_count)
print("Dev test on", len(dev_ent), "entities in", len(dev_art_texts), "articles")
print("Dev instances pos/neg:", dev_pos_count, dev_neg_count)
print()
print(" CUTOFF", self.CUTOFF)
print(" DOC_CUTOFF", self.DOC_CUTOFF)
print(" INPUT_DIM", self.INPUT_DIM)
print(" HIDDEN_1_WIDTH", self.HIDDEN_1_WIDTH)
print(" DESC_WIDTH", self.DESC_WIDTH)
@ -116,6 +126,10 @@ class EL_Model:
print(" DROP", self.DROP)
print()
self._test_dev(dev_ent, dev_gold, dev_desc, dev_art, dev_art_texts, print_string="dev_random", calc_random=True)
self._test_dev(dev_ent, dev_gold, dev_desc, dev_art, dev_art_texts, print_string="dev_pre", avg=True)
print()
start = 0
stop = min(self.BATCH_SIZE, len(train_ent))
processed = 0
@ -125,10 +139,10 @@ class EL_Model:
golds = [train_gold[e] for e in next_batch]
descs = [train_desc[e] for e in next_batch]
articles = [train_texts[train_article[e]] for e in next_batch]
articles = [train_art_texts[train_art[e]] for e in next_batch]
self.update(entities=next_batch, golds=golds, descs=descs, texts=articles)
self._test_dev(dev_ent, dev_gold, dev_desc, dev_article, dev_texts, print_string="dev_inter", avg=True)
self._test_dev(dev_ent, dev_gold, dev_desc, dev_art, dev_art_texts, print_string="dev_inter", avg=True)
processed += len(next_batch)
@ -151,7 +165,7 @@ class EL_Model:
predictions = self._predict(entities=entities, article_docs=article_docs, desc_docs=desc_docs, avg=avg)
# TODO: combine with prior probability
p, r, f, acc = run_el.evaluate(predictions, golds, to_print=False)
p, r, f, acc = run_el.evaluate(predictions, golds, to_print=False, times_hundred=False)
loss, gradient = self.get_loss(self.model.ops.asarray(predictions), self.model.ops.asarray(golds))
print("p/r/F/acc/loss", print_string, round(p, 1), round(r, 1), round(f, 1), round(acc, 2), round(loss, 5))
@ -288,14 +302,18 @@ class EL_Model:
collect_incorrect=True)
local_vectors = list() # TODO: local vectors
text_by_article = dict()
entities = set()
gold_by_entity = dict()
desc_by_entity = dict()
article_by_entity = dict()
entities = list()
text_by_article = dict()
sentence_by_entity = dict()
text_by_sentence = dict()
cnt = 0
next_entity_nr = 0
next_entity_nr = 1
next_sent_nr = 1
files = listdir(training_dir)
shuffle(files)
for f in files:
@ -305,33 +323,81 @@ class EL_Model:
if cnt % 500 == 0 and to_print:
print(datetime.datetime.now(), "processed", cnt, "files in the training dataset")
cnt += 1
if article_id not in text_by_article:
with open(os.path.join(training_dir, f), mode="r", encoding='utf8') as file:
text = file.read()
text_by_article[article_id] = text
# parse the article text
with open(os.path.join(training_dir, f), mode="r", encoding='utf8') as file:
text = file.read()
article_doc = self.nlp(text)
truncated_text = text[0:min(self.DOC_CUTOFF, len(text))]
text_by_article[article_id] = truncated_text
# process all positive and negative entities, collect all relevant mentions in this article
article_terms = set()
entities_by_mention = dict()
for mention, entity_pos in correct_entries[article_id].items():
descr = id_to_descr.get(entity_pos)
if descr:
entities.append(next_entity_nr)
gold_by_entity[next_entity_nr] = 1
desc_by_entity[next_entity_nr] = descr
article_by_entity[next_entity_nr] = article_id
entity = "E_" + str(next_entity_nr) + "_" + article_id + "_" + mention
next_entity_nr += 1
gold_by_entity[entity] = 1
desc_by_entity[entity] = descr
article_terms.add(mention)
mention_entities = entities_by_mention.get(mention, set())
mention_entities.add(entity)
entities_by_mention[mention] = mention_entities
for mention, entity_negs in incorrect_entries[article_id].items():
for entity_neg in entity_negs:
descr = id_to_descr.get(entity_neg)
if descr:
entities.append(next_entity_nr)
gold_by_entity[next_entity_nr] = 0
desc_by_entity[next_entity_nr] = descr
article_by_entity[next_entity_nr] = article_id
entity = "E_" + str(next_entity_nr) + "_" + article_id + "_" + mention
next_entity_nr += 1
gold_by_entity[entity] = 0
desc_by_entity[entity] = descr
article_terms.add(mention)
mention_entities = entities_by_mention.get(mention, set())
mention_entities.add(entity)
entities_by_mention[mention] = mention_entities
# find all matches in the doc for the mentions
# TODO: fix this - doesn't look like all entities are found
matcher = PhraseMatcher(self.nlp.vocab)
patterns = list(self.nlp.tokenizer.pipe(article_terms))
matcher.add("TerminologyList", None, *patterns)
matches = matcher(article_doc)
# store sentences
sentence_to_id = dict()
for match_id, start, end in matches:
span = article_doc[start:end]
sent_text = span.sent
sent_nr = sentence_to_id.get(sent_text, None)
if sent_nr is None:
sent_nr = "S_" + str(next_sent_nr) + article_id
next_sent_nr += 1
text_by_sentence[sent_nr] = sent_text
sentence_to_id[sent_text] = sent_nr
mention_entities = entities_by_mention[span.text]
for entity in mention_entities:
entities.add(entity)
sentence_by_entity[entity] = sent_nr
article_by_entity[entity] = article_id
# remove entities that didn't have all data
gold_by_entity = {k: v for k, v in gold_by_entity.items() if k in entities}
desc_by_entity = {k: v for k, v in desc_by_entity.items() if k in entities}
article_by_entity = {k: v for k, v in article_by_entity.items() if k in entities}
text_by_article = {k: v for k, v in text_by_article.items() if k in article_by_entity.values()}
sentence_by_entity = {k: v for k, v in sentence_by_entity.items() if k in entities}
text_by_sentence = {k: v for k, v in text_by_sentence.items() if k in sentence_by_entity.values()}
if to_print:
print()
print("Processed", cnt, "training articles, dev=" + str(dev))
print()
return entities, gold_by_entity, desc_by_entity, article_by_entity, text_by_article
return list(entities), gold_by_entity, desc_by_entity, article_by_entity, text_by_article, sentence_by_entity, text_by_sentence

2
examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py
View File

@ -111,7 +111,7 @@ if __name__ == "__main__":
print("STEP 6: training", datetime.datetime.now())
my_nlp = spacy.load('en_core_web_md')
trainer = EL_Model(kb=my_kb, nlp=my_nlp)
trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=400, devlimit=50)
trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=100, devlimit=20)
print()
# STEP 7: apply the EL algorithm on the dev dataset