On Term Similarity Measures for Short Text Classification We study term expansion (or document expansion), which is used for classifying documents, especially for short documents such as twitter and blogs on the Web. Term expansion enables us to augment the sparse information in those short documents. Carpineto et al. have proposed a term expansion method based on FCA (Formal Concept Analysis), while Rogers et al. have proposed another term expansion method based on LDA (Latent Dirichlet Allocation). In this paper, we take the notion of weighted term similarity measures in FCA, and examine its effectiveness used for term expansion. We also study the effectiveness of some correlation measures in the field of association rule mining. We perform some experimental study on the effects of the proposed term similarity measures in term expansion using two short text corpora. The experimental results show that those weighted term similarity measures, when choosing an appropriate weight value, outperform the prior methods.

Learning Short Text Representation using NonNegative Matrix Factorization and Word Semantic Correlations With the emergence of online medias, short texts have been increasingly available. Because the short texts have a limited contextual information, they are sparse, noisy and ambiguous. Conventional models to text representation are hence not applicable. In this paper, we propose a representation learning model for short texts to tackle this challenge. Our proposed model is based on NonNegative Matrix Factorization (NNMF). Due to the problem lacking word cooccurrences information, it effectively learns the representation model by incorporating the semantic relationship between words generated by unsupervised learning methods. Finally, the NNMFbased model is effectively solved by using a gradient descentbased algorithm. Experimental results on two benchmark data sets, including sentiment140 and 20newsgroups, demonstrate that our proposed approach achieved the improvement of classification accuracy to stateoftheart topic discovery techniques.