Term relevance dependency model for text classification

Text classification (TC) has long been an important research topic in information retrieval (IR) related areas. Conventional language model (LM)-based TC is solely based on matching the words in the documents and classes by using a naïve Bayes classifier (NBC). In the literature, both the term association model (TA), which further considers word-to-word information, and the relevance model (RM), which further considers word-to-document information, have been shown to outperform a simple LM for IR. In this paper, we study a novel integration of TA with RM for LM-NBC-based TC. The new model is called the term relevance dependency model. In the model, the probability of a word given a class is represented by a term association LM probability learned by a RM framework. The results of TC experiments on the 20newsgroups and Reuters-21578 corpora demonstrate that the new model outperforms the standard NBC and several other LM-NBC-based methods.