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Applying a multitask feature sparsity method for the classification of semantic relations between nominals | IEEE Conference Publication | IEEE Xplore

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


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 sp...Show More

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.
Date of Conference: 15-17 July 2012
Date Added to IEEE Xplore: 24 November 2012
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Conference Location: Xi'an, China

References

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