  Dataless text classification, i.e., a new paradigm of weakly supervised
learning, refers to the task of learning with unlabeled documents and a few
predefined representative words of categories, known as seed words. The recent
generative dataless methods construct document-specific category priors by
using seed word occurrences only, however, such category priors often contain
very limited and even noisy supervised signals. To remedy this problem, in this
paper we propose a novel formulation of category prior. First, for each
document, we consider its label membership degree by not only counting seed
word occurrences, but also using a novel prototype scheme, which captures
pseudo-nearest neighboring categories. Second, for each label, we consider its
frequency prior knowledge of the corpus, which is also a discriminative
knowledge for classification. By incorporating the proposed category prior into
the previous generative dataless method, we suggest a novel generative dataless
method, namely Weakly Supervised Prototype Topic Model (WSPTM). The
experimental results on real-world datasets demonstrate that WSPTM outperforms
the existing baseline methods.
