Open AccessRelation Extraction using Language Model Based on Knowledge Graph
Author(s) -
Chengli Xing,
Xueyang Liu,
Dongdong Du,
HU Wen-hui,
Minghui Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1624/2/022037
Subject(s) - relationship extraction , computer science , natural language processing , leverage (statistics) , artificial intelligence , information extraction , word embedding , knowledge graph , semantic relation , embedding , relation (database) , task (project management) , word (group theory) , graph , natural language understanding , natural language , information retrieval , linguistics , data mining , theoretical computer science , philosophy , cognition , management , neuroscience , economics , biology
Relation extraction is an important task in natural language processing (NLP). The existing methods generally pay more attention on extracting textual semantic information from text, but ignore the relation contextual information from existed relations in datasets, which is very important for the performance of relation extraction task. In this paper, we represent each individual entity as a embedding based on entities and relations knowledge graph, which encodes the relation contextual information between the given entity pairs and relations. Besides, inspired by the impressive performance of language models recently, we used the language model to leverage word semantic information, in which word semantic information can be better captured than word embedding. The experimental results on SemEval2010 Task 8 dataset showed that the F1-score of our proposed method improved nearly 3% compared with the previous methods.