Transferring Topical Knowledge from Auxiliary Long Texts for Short Text Clustering
Nathan N. Liu
Hong Kong University of Science and Technology Clear Water Bay, Hong Kong
NEC Labs China Beijing, China
email@example.com firstname.lastname@example.org email@example.com Yong Yu Qiang Yang
Shanghai Jiao Tong University 800 Dongchuan Road Shanghai, China Hong Kong University of Science and Technology Clear Water Bay, Hong Kong
Shanghai Jiao Tong University 800 Dongchuan Road Shanghai, China
With the rapid growth of social Web applications such as Twitter and online advertisements, the task of understanding short texts is becoming more and more important. Most traditional text mining techniques are designed to handle long text documents. For short text messages, many of the existing techniques are not eﬀective due to the sparseness of text representations. To understand short messages, we observe that it is often possible to ﬁnd topically related long texts, which can be utilized as the auxiliary data when mining the target short texts data. In this article, we present a novel approach to cluster short text messages via transfer learning from auxiliary long text data. We show that while some previous works for enhancing short text clustering with related long texts exist, most of them ignore the semantic and topical inconsistencies between the target and auxiliary data and may hurt the clustering performance on the short texts. To accommodate the possible inconsistencies between source and target data, we propose a novel topic model - Dual Latent Dirichlet Allocation (DLDA) model, which jointly learns two sets of topics on short and long texts and couples the topic parameters to cope with the potential inconsistencies between data sets. We demonstrate through large-scale clustering experiments on both advertisements and Twitter data that we can obtain superior performance over several state-of-art techniques for...