Abstract：Semantic analysis is one of the fundamental and key problems in the research of content-based Text Mining. Most of supervised machine learning methods led to poor performance when work on limited tagged data. This paper investigated a novel semi-supervised learning algorithm—Transductive Support Vector Machine for shallow semantic parsing. An optimization strategy of selecting training instances, based on active learning, was integrated with TSVM. The experiment result shows that the method integrating TSVM and optimization strategy for shallow semantic parsing outperforms supervised methods on small tagged data.
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2008, Vol. 22 
