Data sparseness, the evident characteristic of short text, has always been regarded as the main cause of the low ac- curacy in the classification of short texts using statistical methods. Intensive research has been c...Data sparseness, the evident characteristic of short text, has always been regarded as the main cause of the low ac- curacy in the classification of short texts using statistical methods. Intensive research has been conducted in this area during the past decade. However, most researchers failed to notice that ignoring the semantic importance of certain feature terms might also contribute to low classification accuracy. In this paper we present a new method to tackle the problem by building a strong feature thesaurus （SFT） based on latent Dirichlet allocation （LDA） and information gain （IG） models. By giving larger weights to feature terms in SFT, the classification accuracy can be improved. Specifically, our method appeared to be more effective with more detailed classification. Experiments in two short text datasets demonstrate that our approach achieved improvement compared with the state-of-the-art methods including support vector machine （SVM） and Naive Bayes Multinomial.展开更多
基金Project (No. 20111081023) supported by the Tsinghua University Initiative Scientific Research Program, China
文摘Data sparseness, the evident characteristic of short text, has always been regarded as the main cause of the low ac- curacy in the classification of short texts using statistical methods. Intensive research has been conducted in this area during the past decade. However, most researchers failed to notice that ignoring the semantic importance of certain feature terms might also contribute to low classification accuracy. In this paper we present a new method to tackle the problem by building a strong feature thesaurus （SFT） based on latent Dirichlet allocation （LDA） and information gain （IG） models. By giving larger weights to feature terms in SFT, the classification accuracy can be improved. Specifically, our method appeared to be more effective with more detailed classification. Experiments in two short text datasets demonstrate that our approach achieved improvement compared with the state-of-the-art methods including support vector machine （SVM） and Naive Bayes Multinomial.
