A practical algorithm for solving the sparseness problem of short text clustering

摘要

Dirichlet Multinomial Mixture (DMM) models have been successful in clustering short texts. However, the word co-occurrence information that can be captured by these models is limited to the short text corpus itself. If two words have strong relatedness but rarely co-occurring in short texts, these models can not fully capture the semantic relatedness between the two words. In this paper, we propose a novel model by incorporating word-word correlation into DMM, called WDMM. By constructing a sparse graph using word-word relationship, our model expands each short text using their neighboring words in each text that can help to solve the problem of sparseness in short texts. Therefore, the cluster label of each text is not only influenced by its words, but decided by their similar words in this corpus. Experimental results on real-world datasets demonstrated the substantial superiority of our WDMM model over the state-of-the-art methods.