多任务特征稀疏度方法在名词语义关系分类中的应用

Guoqing Chao, Shiliang Sun
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引用次数: 7

摘要

本文通过主成分分析(peA)提取7个有效特征集,并将其降维到同一维,从而在最大熵判别(MED)框架下利用多任务特征稀疏度方法自动识别英语句子中语料之间的语义关系。该方法可以充分利用不同语义分类之间的相关信息进行多任务判别学习,不需要额外的知识来源。在SemEval 2007中,我们的系统获得了69.15%的f值,高于独立支持向量机的f值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying a multitask feature sparsity method for the classification of semantic relations between nominals
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.
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