  Manually labeling documents is tedious and expensive, but it is essential for
training a traditional text classifier. In recent years, a few dataless text
classification techniques have been proposed to address this problem. However,
existing works mainly center on single-label classification problems, that is,
each document is restricted to belonging to a single category. In this paper,
we propose a novel Seed-guided Multi-label Topic Model, named SMTM. With a few
seed words relevant to each category, SMTM conducts multi-label classification
for a collection of documents without any labeled document. In SMTM, each
category is associated with a single category-topic which covers the meaning of
the category. To accommodate with multi-labeled documents, we explicitly model
the category sparsity in SMTM by using spike and slab prior and weak smoothing
prior. That is, without using any threshold tuning, SMTM automatically selects
the relevant categories for each document. To incorporate the supervision of
the seed words, we propose a seed-guided biased GPU (i.e., generalized Polya
urn) sampling procedure to guide the topic inference of SMTM. Experiments on
two public datasets show that SMTM achieves better classification accuracy than
state-of-the-art alternatives and even outperforms supervised solutions in some
scenarios.
