Automatic Text Categorization of Mathematical Word Problems

摘要: This paper describes a novel application of text categorization for mathematical word problems , namely Multiplicative Compare and Equal Group problems. The empirical results analysis show that common processing techniques such as stopword removal stemming should be selectively used. It is highly beneficial not to remove stopwords do stemming. Part speech tagging also used distinguish words in discriminative parts from the non-discriminative which only fail help but even mislead decision An SVM classifier with these outperforms an default setting (i.e. stemming). Furthermore, probabilistic meta proposed combine weighted two classifiers different problem representations generated by preprocessing techniques. further improves accuracy.