Source code for nlp_architect.data.cdc_resources.relations.word_embedding_relation_extraction

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# Copyright 2017-2018 Intel Corporation
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# Licensed under the Apache License, Version 2.0 (the "License");
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import logging
import math
from typing import List, Set

from scipy.spatial.distance import cosine as cos

from nlp_architect.common.cdc.mention_data import MentionDataLight
from nlp_architect.data.cdc_resources.embedding.embed_elmo import ElmoEmbedding, \
    ElmoEmbeddingOffline
from nlp_architect.data.cdc_resources.embedding.embed_glove import GloveEmbedding, \
    GloveEmbeddingOffline
from nlp_architect.data.cdc_resources.relations.relation_extraction import RelationExtraction
from nlp_architect.data.cdc_resources.relations.relation_types_enums import EmbeddingMethod, \
    RelationType
from nlp_architect.utils.string_utils import StringUtils

logger = logging.getLogger(__name__)


[docs]class WordEmbeddingRelationExtraction(RelationExtraction): def __init__(self, method: EmbeddingMethod = EmbeddingMethod.GLOVE, glove_file: str = None, elmo_file: str = None, cos_accepted_dist: float = 0.7): """ Extract Relation between two mentions according to Word Embedding cosine distance Args: method (optional): EmbeddingMethod.{GLOVE/GLOVE_OFFLINE/ELMO/ELMO_OFFLINE} (default = GLOVE) glove_file (required on GLOVE/GLOVE_OFFLINE mode): str Location of Glove file elmo_file (required on ELMO_OFFLINE mode): str Location of Elmo file """ if method == EmbeddingMethod.GLOVE: self.embedding = GloveEmbedding(glove_file) self.contextual = False elif method == EmbeddingMethod.GLOVE_OFFLINE: self.embedding = GloveEmbeddingOffline(glove_file) self.contextual = False elif method == EmbeddingMethod.ELMO: self.embedding = ElmoEmbedding() self.contextual = True elif method == EmbeddingMethod.ELMO_OFFLINE: self.embedding = ElmoEmbeddingOffline(elmo_file) self.contextual = True self.accepted_dist = cos_accepted_dist super(WordEmbeddingRelationExtraction, self).__init__()
[docs] def extract_all_relations(self, mention_x: MentionDataLight, mention_y: MentionDataLight) -> Set[RelationType]: ret_ = set() ret_.add(self.extract_sub_relations(mention_x, mention_y, RelationType.WORD_EMBEDDING_MATCH)) return ret_
[docs] def extract_sub_relations(self, mention_x: MentionDataLight, mention_y: MentionDataLight, relation: RelationType) -> RelationType: """ Check if input mentions has the given relation between them Args: mention_x: MentionDataLight mention_y: MentionDataLight relation: RelationType Returns: RelationType: relation in case mentions has given relation or RelationType.NO_RELATION_FOUND otherwise """ if relation is not RelationType.WORD_EMBEDDING_MATCH: return RelationType.NO_RELATION_FOUND mention_x_str = mention_x.tokens_str mention_y_str = mention_y.tokens_str if StringUtils.is_pronoun(mention_x_str.lower()) or \ StringUtils.is_pronoun(mention_y_str.lower()): if not self.contextual: return RelationType.NO_RELATION_FOUND if mention_x.mention_context is None or mention_y.mention_context is None: return RelationType.NO_RELATION_FOUND if self.is_word_embed_match(mention_x, mention_y): return RelationType.WORD_EMBEDDING_MATCH return RelationType.NO_RELATION_FOUND
[docs] def is_word_embed_match(self, mention_x: MentionDataLight, mention_y: MentionDataLight): """ Check if input mentions Word Embedding cosine distance below above 0.65 Args: mention_x: MentionDataLight mention_y: MentionDataLight Returns: bool """ match_result = False x_embed = self.embedding.get_head_feature_vector(mention_x) y_embed = self.embedding.get_head_feature_vector(mention_y) # make sure words are not 'unk/None/0' if x_embed is not None and y_embed is not None: dist = cos(x_embed, y_embed) if not math.isnan(dist): sim = 1 - dist if sim >= self.accepted_dist: match_result = True return match_result
[docs] @staticmethod def get_supported_relations() -> List[RelationType]: """ Return all supported relations by this class Returns: List[RelationType] """ return [RelationType.WORD_EMBEDDING_MATCH]