  State-of-the-art weakly supervised text classification methods, while
significantly reduced the required human supervision, still requires the
supervision to cover all the classes of interest. This is never easy to meet in
practice when human explore new, large corpora without complete pictures. In
this paper, we work on a novel yet important problem of weakly supervised
open-world text classification, where supervision is only needed for a few
examples from a few known classes and the machine should handle both known and
unknown classes in test time. General open-world classification has been
studied mostly using image classification; however, existing methods typically
assume the availability of sufficient known-class supervision and strong
unknown-class prior knowledge (e.g., the number and/or data distribution). We
propose a novel framework WOT-Class that lifts those strong assumptions.
Specifically, it follows an iterative process of (a) clustering text to new
classes, (b) mining and ranking indicative words for each class, and (c)
merging redundant classes by using the overlapped indicative words as a bridge.
Extensive experiments on 7 popular text classification datasets demonstrate
that WOT-Class outperforms strong baselines consistently with a large margin,
attaining 23.33% greater average absolute macro-F1 over existing approaches
across all datasets. Such competent accuracy illuminates the practical
potential of further reducing human effort for text classification.
