ClassiNet -- Predicting Missing Features for Short-Text Classification
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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 x⃗, we find similar features from ClassiNet that did not appear in x⃗, and append those features in the representation of x⃗. 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.
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