To address the shortcomings of previous seed word-based approaches, we propose a novel weakly supervised approach, which uses the available seed words in a more effective way.
we consider a student-teacher framework, according to which a bag-of-seed-words classifier (teacher) is applied on unlabeled segments to supervise a second model (student), which can be any supervised model, including neural networks.