Automatic Entity Relation Extraction Based on Conditional Random Fields

Author

Zhang, Suxiang ; Zhang, Suxian ; Gao, Guoyang

Author_Institution

Dept. of Electron. & Commun., North China Electr. Power Univ., Baoding

Volume

2

fYear

2008

fDate

18-20 Oct. 2008

Firstpage

286

Lastpage

290

Abstract

Entity relation extraction (RE) is one of the important research fields in information extraction, we regard RE as a classification problem in this paper. This paper presents a novel approach, conditional random fields (CRFs)-based machine learning is used to extract entity relation between entities from Chinese texts, ten features have been designed for entity relation extraction, which includes morphology, grammar and semantic feature. Experimental results show that the new approach achieve an improved performance on corpus for Chinese entity relation extraction.

Keywords

learning (artificial intelligence); text analysis; Chinese texts; automatic entity relation extraction; conditional random fields; grammar; machine learning; morphology; semantic feature; Data mining; Educational institutions; Feature extraction; Fuzzy systems; Kernel; Learning systems; Machine learning; Morphology; Natural language processing; Natural languages; Conditional Random Fields; entity relation extraction and evaluation; feature select;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on

Conference_Location

Shandong

Print_ISBN

978-0-7695-3305-6

Type

conf

DOI

10.1109/FSKD.2008.261

Filename

4666124