Feature Extension for Short Text Categorization Using Frequent Term Sets

Abstract

A short text feature extension method based on frequent term sets is proposed to overcome the drawbacks of the vector space model (VSM) on representing short text content. After defining the co-occurring and class orientation relations between terms, frequent term sets with identical class orientation are generated by calculating the support and confidence of word sets, and then taken as the background knowledge for short text feature extension. For each single term of the short text, the term sets containing this term are retrieved in the background knowledge and added into the original term vector as the feature extension. The experimental results on Sougou corpus show that the support and confidence have great impact on the scale of the background knowledge, but excessive extension also has redundancy and cannot obtain further improvement. The background knowledge based on frequent term sets is an effective way for feature extension. When the number of the training documents is limited, these extended features can greatly improve the classification results of SVM.