NDLI: Applying a multitask feature sparsity method for the classification of semantic relations between nominals

Content Provider	IEEE Xplore Digital Library
Author	Guoqing Chao Shiliang Sun
Copyright Year	2012
Description	Author affiliation: Department of Computer Science and Technology, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China (Guoqing Chao; Shiliang Sun)
Abstract	This paper extracts seven effective feature sets and reduces them to same dimension by principle component analysis (peA), such that it can utilize a multitask feature sparsity approach to the automatic identification of semantic relations between nominals in English sentences under maximum entropy discrimination (MED) framework. This method can make full use of related information between different semantic classifications to perform multitask discriminative learning and don't employ additional knowledge sources. At SemEval 2007, our system achieved a F-score of 69.15 % which is higher than that by independent SVM.
Starting Page	72
Ending Page	76
File Size	495482
Page Count	5
File Format	PDF
ISBN	9781467314848
ISSN	2160133X
e-ISBN	9781467314879
DOI	10.1109/ICMLC.2012.6358889
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Instruments Sun Abstracts Containers Support vector machines Multitask learning Maximum entropy discrimination Support vector machine Semantic relation
Content Type	Text
Resource Type	Article

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Applying a multitask feature sparsity method for the classification of semantic relations between nominals