Open AccessResearch on Entity Coreference Resolution Technology Oriented to Military Knowledge Graph
Author(s) -
Ruijuan Hu,
Zhufeng Li,
Jian Li
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 - 10.1088/1742-6596/1624/5/052003
Subject(s) - coreference , computer science , natural language processing , resolution (logic) , artificial intelligence , graph , knowledge base , field (mathematics) , construct (python library) , knowledge graph , information retrieval , theoretical computer science , programming language , mathematics , pure mathematics
Data processing in military field is in the stage of fusion disambiguation. The way of addressing the same thing is complex and difficult to integrate. Entity coreference resolution can effectively solve this problem. At the same time, entity coreference resolution is also an important part of knowledge fusion stage in the process of building military knowledge graph. This paper studies and implements Chinese coreference resolution from two aspects: Named Entity Recognition and coreference resolution. Firstly, use the BiLSTM+CRF model of neural network to realize NER in military field. By mining Wikipedia corpus, construct a pattern base, and iteratively find the coreference relationship in text based on pattern, and finally establish a model. To complete the rapid and effective construction of a total of 220,000 thesaurus, covering the military field of aircraft and ships two types of objectives, to achieve the military field entity coreference resolution, to provide strong support for the construction of military knowledge graph.