Title
Research on Domain Term Extraction Based on Conditional Random Fields
Abstract
Domain Term Extraction has an important significance in natural language processing, and it is widely applied in information retrieval, information extraction, data mining, machine translation and other information processing fields. In this paper, an automatic domain term extraction method is proposed based on condition random fields. We treat domain terms extraction as a sequence labeling problem, and terms' distribution characteristics as features of the CRF model. Then we used the CRF tool to train a template for the term extraction. Experimental results showed that the method is simple, with common domains, and good results were achieved. In the open test, the precision rate achieved was 79.63 %, recall rate was 73.54%, and F-measure was 76.46%.
Year
DOI
Venue
2009
10.1007/978-3-642-00831-3_27
ICCPOL
Keywords
Field
DocType
common domain,domain terms extraction,information retrieval,automatic domain term extraction,natural language processing,term extraction,information processing field,crf model,conditional random fields,domain term extraction,crf tool,information extraction,conditional random field,data mining,information processing,machine translation
Conditional random field,Sequence labeling,Random field,Information processing,Pattern recognition,Recall rate,Computer science,Machine translation,Information extraction,Artificial intelligence,Natural language processing,Relationship extraction
Conference
Volume
ISSN
Citations 
5459
0302-9743
2
PageRank 
References 
Authors
0.44
7
3
Name
Order
Citations
PageRank
Dequan Zheng17421.56
Tiejun Zhao2643102.68
Jing Yang320.44