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

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# Copyright 2017-2018 Intel Corporation
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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import logging
import os
from typing import List, Set

from nlp_architect.common.cdc.mention_data import MentionData
from nlp_architect.data.cdc_resources.relations.relation_extraction import RelationExtraction
from nlp_architect.data.cdc_resources.relations.relation_types_enums import RelationType
from nlp_architect.utils.io import load_json_file

logger = logging.getLogger(__name__)


[docs]class WithinDocCoref(RelationExtraction): def __init__(self, wd_file: str): """ Extract Relation between two mentions according to Within document co-reference Args: wd_file (required): str Location of within doc co-reference mentions file """ logger.info('Loading Within doc resource') if wd_file is not None and os.path.isfile(wd_file): wd_mentions_json = load_json_file(wd_file) self.within_doc_coref_chain = self.arrange_resource(wd_mentions_json) else: raise FileNotFoundError('Within-doc resource file not found or not in path') super(WithinDocCoref, self).__init__()
[docs] @staticmethod def arrange_resource(wd_mentions_json): document_tokens_dict = dict() for mention_json in wd_mentions_json: mention_data = MentionData.read_json_mention_data_line(mention_json) mention_tokens = mention_data.tokens_number for i in range(0, len(mention_tokens)): doc_id = mention_data.doc_id sent_id = mention_data.sent_id token_map_key = MentionData.static_gen_token_unique_id(doc_id, sent_id, mention_tokens[i]) document_tokens_dict[token_map_key] = mention_data.coref_chain return document_tokens_dict
[docs] def extract_all_relations(self, mention_x: MentionData, mention_y: MentionData) -> Set[RelationType]: ret_ = set() ret_.add(self.extract_sub_relations(mention_x, mention_y, RelationType.WITHIN_DOC_COREF)) return ret_
[docs] def extract_sub_relations(self, mention_x: MentionData, mention_y: MentionData, 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.WITHIN_DOC_COREF: return RelationType.NO_RELATION_FOUND if mention_x.doc_id == mention_y.doc_id: ment_x_coref_chain = self.extract_within_coref(mention_x) ment_y_coref_chain = self.extract_within_coref(mention_y) if not ment_x_coref_chain or not ment_y_coref_chain: return RelationType.NO_RELATION_FOUND if '-' in ment_x_coref_chain or '-' in ment_y_coref_chain: return RelationType.NO_RELATION_FOUND if set(ment_x_coref_chain) == set(ment_y_coref_chain): return RelationType.WITHIN_DOC_COREF return RelationType.NO_RELATION_FOUND
[docs] def extract_within_coref(self, mention: MentionData) -> List[str]: tokens = mention.tokens_number within_coref_token = [] for token_id in tokens: token_x_id = MentionData.static_gen_token_unique_id(str(mention.doc_id), str(mention.sent_id), str(token_id)) if token_x_id in self.within_doc_coref_chain: token_coref_chain = self.within_doc_coref_chain[token_x_id] if token_coref_chain: within_coref_token.append(token_coref_chain) else: within_coref_token.append('-') break return within_coref_token
[docs] def get_within_doc_coref_chain(self): return self.within_doc_coref_chain
[docs] @staticmethod def create_ment_id(mention_x: MentionData, mention_y: MentionData) -> str: return '_'.join([mention_x.get_mention_id(), mention_y.get_mention_id()])
[docs] @staticmethod def get_supported_relations() -> List[RelationType]: """ Return all supported relations by this class Returns: List[RelationType] """ return [RelationType.WITHIN_DOC_COREF]