Paper Info
Title
Discriminatively Modeling Commonality of Term Types for Extracting Relation from Small Corpora
Abstract
In this paper, we present a novel strategy to partly solve the data sparseness problem caused by small corpora in relation extraction by discriminatively modeling commonality among terms in each term type associated with the relation. The key idea is to use the information of terms rather than that of term pairs to extract relations. Based on this idea, terms in each term type were separately extracted from the corpora and a special function, called relation function, is used to determine whether the two terms selected from each term type have the target relation. As we can get more information of terms than that of term pairs in limited corpora, instances of the target relation we get using commonality among terms will be larger in amount and more reliable in quality. This is also proved by the experiments.
Year
DOI
Venue
2009
10.1109/WI-IAT.2009.275
Web Intelligence/IAT Workshops
Keywords
Field
DocType
target relation,term types,discriminatively modeling commonality,extracting relation,relation extraction,relation function,term pair,data sparseness problem,key idea,limited corpus,term type,special function,small corpora,special functions,computational intelligence,noun,natural language processing,intelligent agent,support vector machines,computational linguistics,data mining,educational technology,discriminative model
Educational technology,Data mining,Intelligent agent,Computational intelligence,Computer science,Computational linguistics,Support vector machine,Natural language processing,Artificial intelligence,Machine learning,Relationship extraction
Conference
Citations 
PageRank 
References 
0
0.34
8
Authors
3
Name
Order
Citations
PageRank
Zhifang Sui117239.06
Yao Liu226.19
Yongwei Hu310.71