docstr: Check if input mentions Word Embedding cosine distance below above 0.65
  
  Args:
      mention_x: MentionDataLight
      mention_y: MentionDataLight
  
  Returns:
      bool
docstr_tok: ["Check","if","input","mentions","Word","Embedding","cosine","distance","below","above","0.65","Args",":","mention_x",":","MentionDataLight","mention_y",":","MentionDataLight","Returns",":","bool"]
code: 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
  
file_path: nlp-architect/nlp_architect/data/cdc_resources/relations/word_embedding_relation_extraction.py
imports: import logging
  import math
  from typing import List, Set
  from scipy.spatial.distance import cosine
  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
label_desc: Use image node_id 4 for calling the WordEmbeddingRelationExtraction obj's underlying member method code with example usage: obj.is_word_embed_match(mention_x, mention_y) and returns: match_result