Short Texts Classification Through Reference Document Expansion

With the rapid development of information technology, short texts arising from socialized human interaction are gradually predominant in network information streams. Accelerating demands are requiring the industry to provide more effective classification of the brief texts. However, faced with short text documents, each of which contains only a few words, traditional document classification models run into difficulty. Aggressive documents expansion works remarkably well for many cases but suffers from the assumption of independent, identically distributed observations. We formalize a view of classification using Bayesian decision theory, treat each short text as observations from a probabilistic model, called a statistical language model, and encode classification preferences with a loss function defined by the language models and the external reference document. According to Vapnik's methods of Structural risk minimization (SRM), the optimal classification action is the one that minimizes the structural risk, which provides a result that allows one to trade off errors on the training sample against improved generalization performance. We conduct experiments by using several corpora of microblog-like data, and analyze the experimental results. With respect to established baselines, results of these experiments show that applying our proposed document expansion method produces better chance to achieve the improved classification performance.