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refactor again to clusters of entities and cosine similarity

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svlandeg 2019-05-28 00:05:22 +02:00
parent 8c4aa076bc
commit 992fa92b66
2 changed files with 206 additions and 224 deletions
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examples/pipeline/wiki_entity_linking/train_el.py
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@ -11,7 +11,7 @@ from thinc.neural._classes.convolution import ExtractWindow
from examples.pipeline.wiki_entity_linking import run_el, training_set_creator, kb_creator from examples.pipeline.wiki_entity_linking import run_el, training_set_creator, kb_creator
from spacy._ml import SpacyVectors, create_default_optimizer, zero_init, logistic, Tok2Vec from spacy._ml import SpacyVectors, create_default_optimizer, zero_init, logistic, Tok2Vec, cosine
from thinc.api import chain, concatenate, flatten_add_lengths, clone, with_flatten from thinc.api import chain, concatenate, flatten_add_lengths, clone, with_flatten
from thinc.v2v import Model, Maxout, Affine, ReLu from thinc.v2v import Model, Maxout, Affine, ReLu
@ -20,6 +20,7 @@ from thinc.t2t import ParametricAttention
from thinc.misc import Residual from thinc.misc import Residual
from thinc.misc import LayerNorm as LN from thinc.misc import LayerNorm as LN
from spacy.cli.pretrain import get_cossim_loss
from spacy.matcher import PhraseMatcher from spacy.matcher import PhraseMatcher
from spacy.tokens import Doc from spacy.tokens import Doc
@ -34,20 +35,20 @@ class EL_Model:
CUTOFF = 0.5 CUTOFF = 0.5
BATCH_SIZE = 5 BATCH_SIZE = 5
UPSAMPLE = True # UPSAMPLE = True
DOC_CUTOFF = 300 # number of characters from the doc context DOC_CUTOFF = 300 # number of characters from the doc context
INPUT_DIM = 300 # dimension of pre-trained vectors INPUT_DIM = 300 # dimension of pre-trained vectors
# HIDDEN_1_WIDTH = 32 # 10 HIDDEN_1_WIDTH = 32
HIDDEN_2_WIDTH = 32 # 6 # HIDDEN_2_WIDTH = 32 # 6
DESC_WIDTH = 64 # 4 DESC_WIDTH = 64
ARTICLE_WIDTH = 64 # 8 ARTICLE_WIDTH = 64
SENT_WIDTH = 64 SENT_WIDTH = 64
DROP = 0.1 DROP = 0.1
LEARN_RATE = 0.0001 LEARN_RATE = 0.0001
EPOCHS = 20 EPOCHS = 10
L2 = 1e-6 L2 = 1e-6
name = "entity_linker" name = "entity_linker"
@ -57,9 +58,10 @@ class EL_Model:
self.nlp = nlp self.nlp = nlp
self.kb = kb self.kb = kb
self._build_cnn(desc_width=self.DESC_WIDTH, self._build_cnn(embed_width=self.INPUT_DIM,
desc_width=self.DESC_WIDTH,
article_width=self.ARTICLE_WIDTH, article_width=self.ARTICLE_WIDTH,
sent_width=self.SENT_WIDTH) sent_width=self.SENT_WIDTH, hidden_1_width=self.HIDDEN_1_WIDTH)
def train_model(self, training_dir, entity_descr_output, trainlimit=None, devlimit=None, to_print=True): def train_model(self, training_dir, entity_descr_output, trainlimit=None, devlimit=None, to_print=True):
# raise errors instead of runtime warnings in case of int/float overflow # raise errors instead of runtime warnings in case of int/float overflow
@ -70,24 +72,28 @@ class EL_Model:
train_ent, train_gold, train_desc, train_art, train_art_texts, train_sent, train_sent_texts = \ 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) self._get_training_data(training_dir, entity_descr_output, False, trainlimit, to_print=False)
train_clusters = list(train_ent.keys())
dev_ent, dev_gold, dev_desc, dev_art, dev_art_texts, dev_sent, dev_sent_texts = \ 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) self._get_training_data(training_dir, entity_descr_output, True, devlimit, to_print=False)
dev_clusters = list(dev_ent.keys())
dev_pos_count = len([g for g in dev_gold.values() if g]) dev_pos_count = len([g for g in dev_gold.values() if g])
dev_neg_count = len([g for g in dev_gold.values() if not g]) dev_neg_count = len([g for g in dev_gold.values() if not g])
# inspect data # inspect data
if self.PRINT_INSPECT: if self.PRINT_INSPECT:
for entity in train_ent: for cluster, entities in train_ent.items():
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() print()
for entity in entities:
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_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] train_neg_entities = [k for k, v in train_gold.items() if not v]
@ -95,29 +101,29 @@ class EL_Model:
train_pos_count = len(train_pos_entities) train_pos_count = len(train_pos_entities)
train_neg_count = len(train_neg_entities) train_neg_count = len(train_neg_entities)
if self.UPSAMPLE: # if self.UPSAMPLE:
if to_print: # if to_print:
print() # print()
print("Upsampling, original training instances pos/neg:", train_pos_count, train_neg_count) # print("Upsampling, original training instances pos/neg:", train_pos_count, train_neg_count)
#
# upsample positives to 50-50 distribution # # upsample positives to 50-50 distribution
while train_pos_count < train_neg_count: # while train_pos_count < train_neg_count:
train_ent.append(random.choice(train_pos_entities)) # train_ent.append(random.choice(train_pos_entities))
train_pos_count += 1 # train_pos_count += 1
#
# upsample negatives to 50-50 distribution # upsample negatives to 50-50 distribution
while train_neg_count < train_pos_count: # while train_neg_count < train_pos_count:
train_ent.append(random.choice(train_neg_entities)) # train_ent.append(random.choice(train_neg_entities))
train_neg_count += 1 # train_neg_count += 1
self._begin_training() self._begin_training()
if to_print: if to_print:
print() print()
print("Training on", len(train_ent), "entities in", len(train_art_texts), "articles") print("Training on", len(train_clusters), "entity clusters in", len(train_art_texts), "articles")
print("Training instances pos/neg:", train_pos_count, train_neg_count) print("Training instances pos/neg:", train_pos_count, train_neg_count)
print() print()
print("Dev test on", len(dev_ent), "entities in", len(dev_art_texts), "articles") print("Dev test on", len(dev_clusters), "entity clusters in", len(dev_art_texts), "articles")
print("Dev instances pos/neg:", dev_pos_count, dev_neg_count) print("Dev instances pos/neg:", dev_pos_count, dev_neg_count)
print() print()
print(" CUTOFF", self.CUTOFF) print(" CUTOFF", self.CUTOFF)
@ -138,94 +144,104 @@ class EL_Model:
self._test_dev(dev_ent, dev_gold, dev_desc, dev_art, dev_art_texts, dev_sent, dev_sent_texts, self._test_dev(dev_ent, dev_gold, dev_desc, dev_art, dev_art_texts, dev_sent, dev_sent_texts,
print_string="dev_pre", avg=True) print_string="dev_pre", avg=True)
print()
processed = 0 processed = 0
for i in range(self.EPOCHS): for i in range(self.EPOCHS):
shuffle(train_ent) shuffle(train_clusters)
start = 0 start = 0
stop = min(self.BATCH_SIZE, len(train_ent)) stop = min(self.BATCH_SIZE, len(train_clusters))
while start < len(train_ent): while start < len(train_clusters):
next_batch = train_ent[start:stop] next_batch = {c: train_ent[c] for c in train_clusters[start:stop]}
processed += len(next_batch.keys())
golds = [train_gold[e] for e in next_batch] self.update(entity_clusters=next_batch, golds=train_gold, descs=train_desc,
descs = [train_desc[e] for e in next_batch] art_texts=train_art_texts, arts=train_art,
article_texts = [train_art_texts[train_art[e]] for e in next_batch] sent_texts=train_sent_texts, sents=train_sent)
sent_texts = [train_sent_texts[train_sent[e]] for e in next_batch]
self.update(entities=next_batch, golds=golds, descs=descs, art_texts=article_texts, sent_texts=sent_texts)
processed += len(next_batch)
start = start + self.BATCH_SIZE start = start + self.BATCH_SIZE
stop = min(stop + self.BATCH_SIZE, len(train_ent)) stop = min(stop + self.BATCH_SIZE, len(train_clusters))
if self.PRINT_TRAIN: if self.PRINT_TRAIN:
print() print()
self._test_dev(train_ent, train_gold, train_desc, train_art, train_art_texts, train_sent, train_sent_texts, self._test_dev(train_ent, train_gold, train_desc, train_art, train_art_texts, train_sent, train_sent_texts,
print_string="train_inter_epoch " + str(i), avg=True) print_string="train_inter_epoch " + str(i), avg=True)
self._test_dev(dev_ent, dev_gold, dev_desc, dev_art, dev_art_texts, dev_sent, dev_sent_texts, self._test_dev(dev_ent, dev_gold, dev_desc, dev_art, dev_art_texts, dev_sent, dev_sent_texts,
print_string="dev_inter_epoch " + str(i), avg=True) print_string="dev_inter_epoch " + str(i), avg=True)
if to_print: if to_print:
print() print()
print("Trained on", processed, "entities across", self.EPOCHS, "epochs") print("Trained on", processed, "entity clusters across", self.EPOCHS, "epochs")
def _test_dev(self, entities, gold_by_entity, desc_by_entity, art_by_entity, art_texts, sent_by_entity, sent_texts, def _test_dev(self, entity_clusters, golds, descs, arts, art_texts, sents, sent_texts,
print_string, avg=True, calc_random=False): print_string, avg=True, calc_random=False):
golds = [gold_by_entity[e] for e in entities]
if calc_random: correct = 0
predictions = self._predict_random(entities=entities) incorrect = 0
else: for cluster, entities in entity_clusters.items():
desc_docs = self.nlp.pipe([desc_by_entity[e] for e in entities]) correct_entities = [e for e in entities if golds[e]]
article_docs = self.nlp.pipe([art_texts[art_by_entity[e]] for e in entities]) incorrect_entities = [e for e in entities if not golds[e]]
sent_docs = self.nlp.pipe([sent_texts[sent_by_entity[e]] for e in entities]) assert len(correct_entities) == 1
predictions = self._predict(entities=entities, article_docs=article_docs, sent_docs=sent_docs,
desc_docs=desc_docs, avg=avg)
# TODO: combine with prior probability entities = list(entities)
p, r, f, acc = run_el.evaluate(predictions, golds, to_print=False, times_hundred=False) shuffle(entities)
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, 2), round(r, 2), round(f, 2), round(acc, 2), round(loss, 2)) if calc_random:
predicted_entity = random.choice(entities)
if predicted_entity in correct_entities:
correct += 1
else:
incorrect += 1
return loss, p, r, f else:
desc_docs = self.nlp.pipe([descs[e] for e in entities])
# article_texts = [art_texts[arts[e]] for e in entities]
def _predict(self, entities, article_docs, sent_docs, desc_docs, avg=True, apply_threshold=True): sent_doc = self.nlp(sent_texts[sents[cluster]])
article_doc = self.nlp(art_texts[arts[cluster]])
predicted_index = self._predict(article_doc=article_doc, sent_doc=sent_doc,
desc_docs=desc_docs, avg=avg)
if entities[predicted_index] in correct_entities:
correct += 1
else:
incorrect += 1
if correct == incorrect == 0:
print("acc", print_string, "NA")
return 0
acc = correct / (correct + incorrect)
print("acc", print_string, round(acc, 2))
return acc
def _predict(self, article_doc, sent_doc, desc_docs, avg=True, apply_threshold=True):
if avg: if avg:
with self.article_encoder.use_params(self.sgd_article.averages) \ with self.article_encoder.use_params(self.sgd_article.averages) \
and self.desc_encoder.use_params(self.sgd_desc.averages): and self.desc_encoder.use_params(self.sgd_desc.averages)\
doc_encodings = self.article_encoder(article_docs) and self.sent_encoder.use_params(self.sgd_sent.averages):
# doc_encoding = self.article_encoder(article_doc)
desc_encodings = self.desc_encoder(desc_docs) desc_encodings = self.desc_encoder(desc_docs)
sent_encodings = self.sent_encoder(sent_docs) sent_encoding = self.sent_encoder([sent_doc])
else: else:
doc_encodings = self.article_encoder(article_docs) # doc_encodings = self.article_encoder(article_docs)
desc_encodings = self.desc_encoder(desc_docs) desc_encodings = self.desc_encoder(desc_docs)
sent_encodings = self.sent_encoder(sent_docs) sent_encoding = self.sent_encoder([sent_doc])
concat_encodings = [list(doc_encodings[i]) + list(sent_encodings[i]) + list(desc_encodings[i]) for i in sent_enc = np.transpose(sent_encoding)
range(len(entities))] highest_sim = -5
best_i = -1
for i, desc_enc in enumerate(desc_encodings):
sim = cosine(desc_enc, sent_enc)
if sim >= highest_sim:
best_i = i
highest_sim = sim
np_array_list = np.asarray(concat_encodings) return best_i
if avg:
with self.model.use_params(self.sgd.averages):
predictions = self.model(np_array_list)
else:
predictions = self.model(np_array_list)
predictions = self.model.ops.flatten(predictions)
predictions = [float(p) for p in predictions]
if apply_threshold:
predictions = [float(1.0) if p > self.CUTOFF else float(0.0) for p in predictions]
return predictions
def _predict_random(self, entities, apply_threshold=True): def _predict_random(self, entities, apply_threshold=True):
if not apply_threshold: if not apply_threshold:
@ -233,47 +249,23 @@ class EL_Model:
else: else:
return [float(1.0) if random.uniform(0, 1) > self.CUTOFF else float(0.0) for _ in entities] return [float(1.0) if random.uniform(0, 1) > self.CUTOFF else float(0.0) for _ in entities]
def _build_cnn_depr(self, embed_width, desc_width, article_width, sent_width, hidden_1_width, hidden_2_width): def _build_cnn(self, embed_width, desc_width, article_width, sent_width, hidden_1_width):
with Model.define_operators({">>": chain, "|": concatenate, "**": clone}): with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
self.desc_encoder = self._encoder_depr(in_width=embed_width, hidden_with=hidden_1_width, end_width=desc_width) self.desc_encoder = self._encoder(in_width=embed_width, hidden_with=hidden_1_width,
self.article_encoder = self._encoder_depr(in_width=embed_width, hidden_with=hidden_1_width, end_width=article_width) end_width=desc_width)
self.sent_encoder = self._encoder_depr(in_width=embed_width, hidden_with=hidden_1_width, end_width=sent_width) self.article_encoder = self._encoder(in_width=embed_width, hidden_with=hidden_1_width,
end_width=article_width)
self.sent_encoder = self._encoder(in_width=embed_width, hidden_with=hidden_1_width,
end_width=sent_width)
in_width = article_width + sent_width + desc_width # def _encoder(self, width):
out_width = hidden_2_width # tok2vec = Tok2Vec(width=width, embed_size=2000, pretrained_vectors=self.nlp.vocab.vectors.name, cnn_maxout_pieces=3,
# subword_features=False, conv_depth=4, bilstm_depth=0)
self.model = Affine(out_width, in_width) \ #
>> LN(Maxout(out_width, out_width)) \ # return tok2vec >> flatten_add_lengths >> Pooling(mean_pool)
>> Affine(1, out_width) \
>> logistic
def _build_cnn(self, desc_width, article_width, sent_width):
with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
self.desc_encoder = self._encoder(width=desc_width)
self.article_encoder = self._encoder(width=article_width)
self.sent_encoder = self._encoder(width=sent_width)
in_width = desc_width + article_width + sent_width
self.model = Affine(self.HIDDEN_2_WIDTH, in_width) \
>> LN(Maxout(self.HIDDEN_2_WIDTH, self.HIDDEN_2_WIDTH)) \
>> Affine(1, self.HIDDEN_2_WIDTH) \
>> logistic
# output_layer = (
# zero_init(Affine(1, in_width, drop_factor=0.0)) >> logistic
# )
# self.model = output_layer
self.model.nO = 1
def _encoder(self, width):
tok2vec = Tok2Vec(width=width, embed_size=2000, pretrained_vectors=self.nlp.vocab.vectors.name, cnn_maxout_pieces=3,
subword_features=False, conv_depth=4, bilstm_depth=0)
return tok2vec >> flatten_add_lengths >> Pooling(mean_pool)
@staticmethod @staticmethod
def _encoder_depr(in_width, hidden_with, end_width): def _encoder(in_width, hidden_with, end_width):
conv_depth = 2 conv_depth = 2
cnn_maxout_pieces = 3 cnn_maxout_pieces = 3
@ -307,64 +299,58 @@ class EL_Model:
self.sgd_desc.learn_rate = self.LEARN_RATE self.sgd_desc.learn_rate = self.LEARN_RATE
self.sgd_desc.L2 = self.L2 self.sgd_desc.L2 = self.L2
self.sgd = create_default_optimizer(self.model.ops) # self.sgd = create_default_optimizer(self.model.ops)
self.sgd.learn_rate = self.LEARN_RATE # self.sgd.learn_rate = self.LEARN_RATE
self.sgd.L2 = self.L2 # self.sgd.L2 = self.L2
@staticmethod @staticmethod
def get_loss(predictions, golds): def get_loss(predictions, golds):
d_scores = (predictions - golds) loss, gradients = get_cossim_loss(predictions, golds)
gradient = d_scores.mean() return loss, gradients
loss = (d_scores ** 2).mean()
return loss, gradient
def update(self, entities, golds, descs, art_texts, sent_texts): def update(self, entity_clusters, golds, descs, art_texts, arts, sent_texts, sents):
golds = self.model.ops.asarray(golds) for cluster, entities in entity_clusters.items():
correct_entities = [e for e in entities if golds[e]]
incorrect_entities = [e for e in entities if not golds[e]]
art_docs = self.nlp.pipe(art_texts) assert len(correct_entities) == 1
sent_docs = self.nlp.pipe(sent_texts) entities = list(entities)
desc_docs = self.nlp.pipe(descs) shuffle(entities)
doc_encodings, bp_doc = self.article_encoder.begin_update(art_docs, drop=self.DROP) # article_text = art_texts[arts[cluster]]
sent_encodings, bp_sent = self.sent_encoder.begin_update(sent_docs, drop=self.DROP) cluster_sent = sent_texts[sents[cluster]]
desc_encodings, bp_desc = self.desc_encoder.begin_update(desc_docs, drop=self.DROP)
concat_encodings = [list(doc_encodings[i]) + list(sent_encodings[i]) + list(desc_encodings[i]) # art_docs = self.nlp.pipe(article_text)
for i in range(len(entities))] sent_doc = self.nlp(cluster_sent)
predictions, bp_model = self.model.begin_update(np.asarray(concat_encodings), drop=self.DROP) for e in entities:
predictions = self.model.ops.flatten(predictions) if golds[e]:
# TODO: more appropriate loss for the whole cluster (currently only pos entities)
# TODO: speed up
desc_doc = self.nlp(descs[e])
# print("entities", entities) # doc_encodings, bp_doc = self.article_encoder.begin_update(art_docs, drop=self.DROP)
# print("predictions", predictions) sent_encodings, bp_sent = self.sent_encoder.begin_update([sent_doc], drop=self.DROP)
# print("golds", golds) desc_encodings, bp_desc = self.desc_encoder.begin_update([desc_doc], drop=self.DROP)
loss, gradient = self.get_loss(predictions, golds) sent_encoding = sent_encodings[0]
desc_encoding = desc_encodings[0]
gradient = float(gradient) sent_enc = self.sent_encoder.ops.asarray([sent_encoding])
# print("gradient", gradient) desc_enc = self.sent_encoder.ops.asarray([desc_encoding])
# print("loss", loss)
model_gradient = bp_model(gradient, sgd=self.sgd) # print("sent_encoding", type(sent_encoding), sent_encoding)
# print("model_gradient", model_gradient) # print("desc_encoding", type(desc_encoding), desc_encoding)
# print("getting los for entity", e)
# concat = doc + sent + desc, but doc is the same within this function loss, gradient = self.get_loss(sent_enc, desc_enc)
sent_start = self.ARTICLE_WIDTH
desc_start = self.ARTICLE_WIDTH + self.SENT_WIDTH
doc_gradient = model_gradient[0][0:sent_start]
sent_gradients = list()
desc_gradients = list()
for x in model_gradient:
sent_gradients.append(list(x[sent_start:desc_start]))
desc_gradients.append(list(x[desc_start:]))
# print("doc_gradient", doc_gradient) # print("gradient", gradient)
# print("sent_gradients", sent_gradients) # print("loss", loss)
# print("desc_gradients", desc_gradients)
bp_doc([doc_gradient], sgd=self.sgd_article) bp_sent(gradient, sgd=self.sgd_sent)
bp_sent(sent_gradients, sgd=self.sgd_sent) # bp_desc(desc_gradients, sgd=self.sgd_desc) TODO
bp_desc(desc_gradients, sgd=self.sgd_desc) # print()
def _get_training_data(self, training_dir, entity_descr_output, dev, limit, to_print): def _get_training_data(self, training_dir, entity_descr_output, dev, limit, to_print):
id_to_descr = kb_creator._get_id_to_description(entity_descr_output) id_to_descr = kb_creator._get_id_to_description(entity_descr_output)
@ -373,13 +359,14 @@ class EL_Model:
collect_correct=True, collect_correct=True,
collect_incorrect=True) collect_incorrect=True)
entities = set() entities_by_cluster = dict()
gold_by_entity = dict() gold_by_entity = dict()
desc_by_entity = dict() desc_by_entity = dict()
article_by_entity = dict() article_by_cluster = dict()
text_by_article = dict() text_by_article = dict()
sentence_by_entity = dict() sentence_by_cluster = dict()
text_by_sentence = dict() text_by_sentence = dict()
sentence_by_text = dict()
cnt = 0 cnt = 0
next_entity_nr = 1 next_entity_nr = 1
@ -402,74 +389,69 @@ class EL_Model:
text_by_article[article_id] = truncated_text text_by_article[article_id] = truncated_text
# process all positive and negative entities, collect all relevant mentions in this article # 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(): for mention, entity_pos in correct_entries[article_id].items():
cluster = article_id + "_" + mention
descr = id_to_descr.get(entity_pos) descr = id_to_descr.get(entity_pos)
entities = set()
if descr: if descr:
entity = "E_" + str(next_entity_nr) + "_" + article_id + "_" + mention entity = "E_" + str(next_entity_nr) + "_" + cluster
next_entity_nr += 1 next_entity_nr += 1
gold_by_entity[entity] = 1 gold_by_entity[entity] = 1
desc_by_entity[entity] = descr 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:
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.text
sent_nr = sentence_to_id.get(sent_text, None)
mention = span.text
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[mention]
for entity in mention_entities:
entities.add(entity) entities.add(entity)
sentence_by_entity[entity] = sent_nr
article_by_entity[entity] = article_id
# remove entities that didn't have all data entity_negs = incorrect_entries[article_id][mention]
gold_by_entity = {k: v for k, v in gold_by_entity.items() if k in entities} for entity_neg in entity_negs:
desc_by_entity = {k: v for k, v in desc_by_entity.items() if k in entities} descr = id_to_descr.get(entity_neg)
if descr:
entity = "E_" + str(next_entity_nr) + "_" + cluster
next_entity_nr += 1
gold_by_entity[entity] = 0
desc_by_entity[entity] = descr
entities.add(entity)
article_by_entity = {k: v for k, v in article_by_entity.items() if k in entities} found_matches = 0
text_by_article = {k: v for k, v in text_by_article.items() if k in article_by_entity.values()} if len(entities) > 1:
entities_by_cluster[cluster] = 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([mention]))
matcher.add("TerminologyList", None, *patterns)
matches = matcher(article_doc)
# store sentences
for match_id, start, end in matches:
found_matches += 1
span = article_doc[start:end]
assert mention == span.text
sent_text = span.sent.text
sent_nr = sentence_by_text.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_by_text[sent_text] = sent_nr
article_by_cluster[cluster] = article_id
sentence_by_cluster[cluster] = sent_nr
if found_matches == 0:
# TODO print("Could not find neg instances or sentence matches for", mention, "in", article_id)
entities_by_cluster.pop(cluster, None)
article_by_cluster.pop(cluster, None)
sentence_by_cluster.pop(cluster, None)
for entity in entities:
gold_by_entity.pop(entity, None)
desc_by_entity.pop(entity, None)
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: if to_print:
print() print()
print("Processed", cnt, "training articles, dev=" + str(dev)) print("Processed", cnt, "training articles, dev=" + str(dev))
print() print()
return list(entities), gold_by_entity, desc_by_entity, article_by_entity, text_by_article, \ return entities_by_cluster, gold_by_entity, desc_by_entity, article_by_cluster, text_by_article, \
sentence_by_entity, text_by_sentence sentence_by_cluster, 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()) print("STEP 6: training", datetime.datetime.now())
my_nlp = spacy.load('en_core_web_md') my_nlp = spacy.load('en_core_web_md')
trainer = EL_Model(kb=my_kb, nlp=my_nlp) trainer = EL_Model(kb=my_kb, nlp=my_nlp)
trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=100, devlimit=20) trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=1000, devlimit=100)
print() print()
# STEP 7: apply the EL algorithm on the dev dataset # STEP 7: apply the EL algorithm on the dev dataset