Paper Info
Title
Chinese Entity Relation Extraction Based on Syntactic Features
Abstract
Entity Relation Extraction (ERE) is an important research topic in the field of information extraction. However, to the best of our knowledge, only a few ERE works have been done for Chinese corpus. Because the syntactic features of Chinese sentences and English sentences are very different, existing algorithms for English corpus cannot be directly applied to Chinese corpus. Thus, in this paper, we propose a novel Chinese entity extraction system based on syntactic features (named SF-CERE). The basic idea of SF-CERE is given as follows. Firstly, we extract candidate relation triples based on verbs and verb-nouns as relation keywords to avoid pre-defining relation types. Secondly, the triples are filtered using the positional constraints between relation keywords and entity pairs. Thirdly, we summarize four major Chinese syntactic features to expand the identified relation triples and improve accuracy. Finally, we use the method of relation transfer to mine and infer implicit relation triples. The experimental results on two real-world dataset (i.e., the encyclopedia dataset and the news dataset) show that SF-CERE effectively improves the quality of the relation triples and obtains good extraction performance.
Year
DOI
Venue
2018
10.1109/ICBK.2018.00021
2018 IEEE International Conference on Big Knowledge (ICBK)
Keywords
Field
DocType
entity relation extraction, syntactic features, relation transfer, sort and filter, relation triples
Computer science,Information extraction,Artificial intelligence,Natural language processing,Encyclopedia,Syntax,Relationship extraction
Conference
ISBN
Citations 
PageRank 
978-1-5386-9126-7
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Yishun Jiang100.34
Gong-Qing Wu213613.07
Chenyang Bu3479.18
Xuegang Hu444244.50