mirror of https://github.com/explosion/spaCy.git
add line that got removed from EntityLinker
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@ -1302,71 +1302,72 @@ class EntityLinker(Pipe):
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# Looping through each sentence and each entity
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# This may go wrong if there are entities across sentences - which shouldn't happen normally.
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for sent_index, sent in enumerate(sentences):
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# get n_neightbour sentences, clipped to the length of the document
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start_sentence = max(0, sent_index - self.n_sents)
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end_sentence = min(len(sentences) -1, sent_index + self.n_sents)
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if sent.ents:
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# get n_neightbour sentences, clipped to the length of the document
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start_sentence = max(0, sent_index - self.n_sents)
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end_sentence = min(len(sentences) -1, sent_index + self.n_sents)
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start_token = sentences[start_sentence].start
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end_token = sentences[end_sentence].end
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start_token = sentences[start_sentence].start
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end_token = sentences[end_sentence].end
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sent_doc = doc[start_token:end_token].as_doc()
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# currently, the context is the same for each entity in a sentence (should be refined)
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sentence_encoding = self.model.predict([sent_doc])[0]
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xp = get_array_module(sentence_encoding)
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sentence_encoding_t = sentence_encoding.T
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sentence_norm = xp.linalg.norm(sentence_encoding_t)
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sent_doc = doc[start_token:end_token].as_doc()
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# currently, the context is the same for each entity in a sentence (should be refined)
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sentence_encoding = self.model.predict([sent_doc])[0]
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xp = get_array_module(sentence_encoding)
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sentence_encoding_t = sentence_encoding.T
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sentence_norm = xp.linalg.norm(sentence_encoding_t)
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for ent in sent.ents:
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entity_count += 1
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for ent in sent.ents:
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entity_count += 1
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to_discard = self.cfg.get("labels_discard", [])
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if to_discard and ent.label_ in to_discard:
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# ignoring this entity - setting to NIL
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final_kb_ids.append(self.NIL)
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final_tensors.append(sentence_encoding)
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else:
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candidates = self.kb.get_candidates(ent.text)
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if not candidates:
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# no prediction possible for this entity - setting to NIL
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to_discard = self.cfg.get("labels_discard", [])
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if to_discard and ent.label_ in to_discard:
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# ignoring this entity - setting to NIL
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final_kb_ids.append(self.NIL)
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final_tensors.append(sentence_encoding)
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elif len(candidates) == 1:
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# shortcut for efficiency reasons: take the 1 candidate
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# TODO: thresholding
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final_kb_ids.append(candidates[0].entity_)
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final_tensors.append(sentence_encoding)
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else:
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random.shuffle(candidates)
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candidates = self.kb.get_candidates(ent.text)
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if not candidates:
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# no prediction possible for this entity - setting to NIL
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final_kb_ids.append(self.NIL)
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final_tensors.append(sentence_encoding)
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# this will set all prior probabilities to 0 if they should be excluded from the model
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prior_probs = xp.asarray([c.prior_prob for c in candidates])
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if not self.cfg.get("incl_prior", True):
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prior_probs = xp.asarray([0.0 for c in candidates])
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scores = prior_probs
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elif len(candidates) == 1:
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# shortcut for efficiency reasons: take the 1 candidate
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# add in similarity from the context
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if self.cfg.get("incl_context", True):
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entity_encodings = xp.asarray([c.entity_vector for c in candidates])
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entity_norm = xp.linalg.norm(entity_encodings, axis=1)
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# TODO: thresholding
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final_kb_ids.append(candidates[0].entity_)
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final_tensors.append(sentence_encoding)
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if len(entity_encodings) != len(prior_probs):
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raise RuntimeError(Errors.E147.format(method="predict", msg="vectors not of equal length"))
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else:
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random.shuffle(candidates)
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# cosine similarity
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sims = xp.dot(entity_encodings, sentence_encoding_t) / (sentence_norm * entity_norm)
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if sims.shape != prior_probs.shape:
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raise ValueError(Errors.E161)
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scores = prior_probs + sims - (prior_probs*sims)
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# this will set all prior probabilities to 0 if they should be excluded from the model
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prior_probs = xp.asarray([c.prior_prob for c in candidates])
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if not self.cfg.get("incl_prior", True):
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prior_probs = xp.asarray([0.0 for c in candidates])
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scores = prior_probs
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# TODO: thresholding
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best_index = scores.argmax().item()
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best_candidate = candidates[best_index]
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final_kb_ids.append(best_candidate.entity_)
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final_tensors.append(sentence_encoding)
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# add in similarity from the context
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if self.cfg.get("incl_context", True):
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entity_encodings = xp.asarray([c.entity_vector for c in candidates])
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entity_norm = xp.linalg.norm(entity_encodings, axis=1)
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if len(entity_encodings) != len(prior_probs):
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raise RuntimeError(Errors.E147.format(method="predict", msg="vectors not of equal length"))
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# cosine similarity
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sims = xp.dot(entity_encodings, sentence_encoding_t) / (sentence_norm * entity_norm)
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if sims.shape != prior_probs.shape:
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raise ValueError(Errors.E161)
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scores = prior_probs + sims - (prior_probs*sims)
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# TODO: thresholding
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best_index = scores.argmax().item()
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best_candidate = candidates[best_index]
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final_kb_ids.append(best_candidate.entity_)
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final_tensors.append(sentence_encoding)
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if not (len(final_tensors) == len(final_kb_ids) == entity_count):
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raise RuntimeError(Errors.E147.format(method="predict", msg="result variables not of equal length"))
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