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Dataless text classification, i.e., a new paradigm of weakly supervisedlearning, refers to the task of learning with unlabeled documents and a fewpredefined representative words of categories, known as seed words. The recentgenerative dataless methods construct document-specific category priors byusing seed word occurrences only, however, such category priors often containvery limited and even noisy supervised signals. To remedy this problem, in thispaper we propose a novel formulation of category prior. First, for eachdocument, we consider its label membership degree by not only counting seedword occurrences, but also using a novel prototype scheme, which capturespseudo-nearest neighboring categories. Second, for each label, we consider itsfrequency prior knowledge of the corpus, which is also a discriminativeknowledge for classification. By incorporating the proposed category prior intothe previous generative dataless method, we suggest a novel generative datalessmethod, namely Weakly Supervised Prototype Topic Model (WSPTM). Theexperimental results on real-world datasets demonstrate that WSPTM outperformsthe existing baseline methods.

Weakly Supervised Prototype Topic Model with Discriminative Seed Words: Modifying the Category Prior by Self-exploring Supervised Signals