JACIII Vol.22 No.4 pp. 475-482
doi: 10.20965/jaciii.2018.p0475


Research on Pattern Representation Based on Keyword and Word Embedding in Chinese Entity Relation Extraction

Feiyue Ye and Zhentao Qin

School of Computer Engineering and Science, Shanghai University
99 Shangda Road, Baoshan District, Shanghai 200444, China

August 30, 2017
April 3, 2018
July 20, 2018
Chinese entity relation extraction, pattern representation, keywords, word embedding
Research on Pattern Representation Based on Keyword and Word Embedding in Chinese Entity Relation Extraction

By using keyword and word embedding for pattern representation in relation extraction, we get more and better Chinese entity relations.

With the rapid development of the Internet, it is becoming more and more important to extract the relationship between the entity from the massive network text and then to build the knowledge graph or the knowledge base. In this paper, we focus on the research of the pattern representation in relation extraction, and extract the high accuracy Chinese entity pairs from large scale web texts. Past relation patterns only consider shallow lexical and syntax, not accurately and deeply express pattern context information, and do not consider keywords information. According to the new entity relation extraction technology and the characteristics of Chinese corpora, we define pattern representation based on keywords and word embedding information, extract deep semantic feature of context information, and strengthen keywords information effect for relation extraction. In addition, we propose a method for obtaining sentence keyword based on word embedding. In the experiment, we use Chinese Hudong Encyclopedia corpus to implement the character relation extraction system, and test the character relation extraction effect. The experimental results show that this method effectively improves the quality of the pattern, and obtains a favorable relation extraction performance.

Cite this article as:
F. Ye and Z. Qin, “Research on Pattern Representation Based on Keyword and Word Embedding in Chinese Entity Relation Extraction,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.4, pp. 475-482, 2018.
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Last updated on Aug. 16, 2018