Source code for nlp_architect.common.cdc.mention_data

# ******************************************************************************
# Copyright 2017-2018 Intel Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# ******************************************************************************
import sys
from typing import List

from import load_json_file
from nlp_architect.utils.string_utils import StringUtils

[docs]class MentionDataLight(object): def __init__( self, tokens_str: str, mention_context: str = None, mention_head: str = None, mention_head_lemma: str = None, mention_pos: str = None, mention_ner: str = None, ): """ Object represent a mention with only text values Args: tokens_str: str the tokens combine text (join with space) mention_head: str mention_head_lemma: str """ self.tokens_str = tokens_str self.mention_context = mention_context if not mention_head and not mention_head_lemma: ( self.mention_head, self.mention_head_lemma, self.mention_head_pos, self.mention_ner, ) = StringUtils.find_head_lemma_pos_ner(str(tokens_str)) else: self.mention_head = mention_head self.mention_head_lemma = mention_head_lemma self.mention_head_pos = mention_pos self.mention_ner = mention_ner
[docs]class MentionData(MentionDataLight): def __init__( self, topic_id: str, doc_id: str, sent_id: int, tokens_numbers: List[int], tokens_str: str, mention_context: List[str], mention_head: str, mention_head_lemma: str, coref_chain: str, mention_type: str = "NA", is_continuous: bool = True, is_singleton: bool = False, score: float = float(-1), predicted_coref_chain: str = None, mention_pos: str = None, mention_ner: str = None, mention_index: int = -1, ) -> None: """ Object represent a mention Args: topic_id: str topic ID doc_id: str document ID sent_id: int sentence number tokens_numbers: List[int] - tokens numbers mention_context: List[str] - list of tokens strings coref_chain: str mention_type: str one of (HUM/NON/TIM/LOC/ACT/NEG) is_continuous: bool is_singleton: bool score: float predicted_coref_chain: str (should be field while evaluated) mention_pos: str mention_ner: str mention_index: in case order is of value (default = -1) """ super(MentionData, self).__init__( tokens_str, mention_context, mention_head, mention_head_lemma, mention_pos, mention_ner ) self.topic_id = topic_id self.doc_id = doc_id self.sent_id = sent_id self.tokens_number = tokens_numbers self.mention_type = mention_type self.coref_chain = coref_chain self.is_continuous = is_continuous self.is_singleton = is_singleton self.score = score self.predicted_coref_chain = predicted_coref_chain self.mention_id = self.gen_mention_id() self.mention_index = mention_index
[docs] @staticmethod def read_json_mention_data_line(mention_line: str): """ Args: mention_line: a Json representation of a single mention Returns: MentionData object """ # pylint: disable=too-many-branches try: topic_id = None coref_chain = None doc_id = None sent_id = None tokens_numbers = None score = -1 mention_type = None predicted_coref_chain = None mention_context = None is_continue = False is_singleton = False mention_pos = None mention_ner = None mention_index = -1 mention_text = mention_line["tokens_str"] if "topic_id" in mention_line: topic_id = mention_line["topic_id"] if "coref_chain" in mention_line: coref_chain = mention_line["coref_chain"] if "doc_id" in mention_line: doc_id = mention_line["doc_id"] if ".xml" not in doc_id: doc_id = doc_id + ".xml" if "sent_id" in mention_line: sent_id = mention_line["sent_id"] if "tokens_number" in mention_line: tokens_numbers = mention_line["tokens_number"] if "mention_context" in mention_line: mention_context = mention_line["mention_context"] if "mention_head" in mention_line and "mention_head_lemma" in mention_line: mention_head = mention_line["mention_head"] mention_head_lemma = mention_line["mention_head_lemma"] if "mention_head_pos" in mention_line: mention_pos = mention_line["mention_head_pos"] if "mention_ner" in mention_line: mention_ner = mention_line["mention_ner"] else: ( mention_head, mention_head_lemma, mention_pos, mention_ner, ) = StringUtils.find_head_lemma_pos_ner(str(mention_text)) if "mention_type" in mention_line: mention_type = mention_line["mention_type"] if "score" in mention_line: score = mention_line["score"] if "is_continuous" in mention_line: is_continue = mention_line["is_continuous"] if "is_singleton" in mention_line: is_singleton = mention_line["is_singleton"] if "predicted_coref_chain" in mention_line: predicted_coref_chain = mention_line["predicted_coref_chain"] if "mention_index" in mention_line: mention_index = mention_line["mention_index"] mention_data = MentionData( topic_id, doc_id, sent_id, tokens_numbers, mention_text, mention_context, mention_head, mention_head_lemma, coref_chain, mention_type, is_continue, is_singleton, score, predicted_coref_chain, mention_pos, mention_ner, mention_index, ) except Exception: print("Unexpected error:", sys.exc_info()[0]) raise Exception("failed reading json line-" + str(mention_line)) return mention_data
[docs] @staticmethod def read_mentions_json_to_mentions_data_list(mentions_json_file: str): """ Args: mentions_json_file: the path of the mentions json file to read Returns: List[MentionData] """ all_mentions_only = load_json_file(mentions_json_file) mentions = [] for mention_line in all_mentions_only: mention_data = MentionData.read_json_mention_data_line(mention_line) mentions.append(mention_data) return mentions
[docs] def get_tokens(self): return self.tokens_number
[docs] def gen_mention_id(self) -> str: if self.doc_id and self.sent_id is not None and self.tokens_number: tokens_ids = [str(self.doc_id), str(self.sent_id)] tokens_ids.extend([str(token_id) for token_id in self.tokens_number]) return "_".join(tokens_ids) return "_".join(self.tokens_str.split())
[docs] def get_mention_id(self) -> str: if not self.mention_id: self.mention_id = self.gen_mention_id() return self.mention_id
[docs] @staticmethod def static_gen_token_unique_id(doc_id: int, sent_id: int, token_id: int) -> str: return "_".join([str(doc_id), str(sent_id), str(token_id)])