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__()
def __init__(self, method: EmbeddingMethod, glove_file: str = None, elmo_file: str = None):
"""
Extract Relation between two mentions according to Word Embedding cosine distance
Args:
method (required): EmbeddingMethod.{GLOVE/GLOVE_OFFLINE/ELMO/ELMO_OFFLINE}
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)
elif method == EmbeddingMethod.GLOVE_OFFLINE:
self.embedding = GloveEmbeddingOffline(glove_file)
elif method == EmbeddingMethod.ELMO:
self.embedding = ElmoEmbedding()
elif method == EmbeddingMethod.ELMO_OFFLINE:
self.embedding = ElmoEmbeddingOffline(elmo_file)
super(WordEmbeddingRelationExtraction, self).__init__()
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__()
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_
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
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
@staticmethod
def get_supported_relations() -> List[RelationType]:
"""
Return all supported relations by this class
Returns:
List[RelationType]
"""
return [RelationType.WORD_EMBEDDING_MATCH]