ClassiNet - Predicting Missing Features for Short-Text Classification

Short and sparse texts such as tweets, search engine snippets, product reviews, and chat messages are abundant on the Web. Classifying such short-texts into a pre-defined set of categories is a common problem that arises in various contexts, such as sentiment classification, spam detection, and information recommendation. The fundamental problem in short-text classification is feature sparseness - the lack of feature overlap between a trained model and a test instance to be classified. We propose ClassiNet - a network of classifiers trained for predicting missing features in a given instance, to overcome the feature sparseness problem. Using a set of unlabeled training instances, we first learn binary classifiers as feature predictors for predicting whether a particular feature occurs in a given instance. Next, each feature predictor is represented as a vertex v(i) in the ClassiNet, where a one-to-one correspondence exists between feature predictors and vertices. The weight of the directed edge e(ij) connecting a vertex v(i) to a vertex v(j) represents the conditional probability that given v(i) exists in an instance, v(j) also exists in the same instance. We show that ClassiNets generalize word co-occurrence graphs by considering implicit co-occurrences between features. We extract numerous features from the trained ClassiNet to overcome feature sparseness. In particular, for a given instance x, we find similar features from ClassiNet that did not appear in x, and append those features in the representation of x. Moreover, we propose a method based on graph propagation to find features that are indirectly related to a given short-text. We evaluate ClassiNets on several benchmark datasets for short-text classification. Our experimental results show that by using ClassiNet, we can statistically significantly improve the accuracy in short-text classification tasks, without having to use any external resources such as thesauri for finding related features.