Word Sense Disambiguation by Combining Labeled Data Expansion and Semi-Supervised Learning Method

摘要

Lack of training data is one of the severest problems facing word sense disambiguation (WSD). To overcome the problem, we propose a method that combines automatic labeled data expansion (Step-1) and semi-supervised learning (Step-2). The Step-1 and 2 methods are both effective, but their combination has a synergistic effect.