  We propose KDSL, a new word sense disambiguation (WSD) framework that
utilizes knowledge to automatically generate sense-labeled data for supervised
learning. First, from WordNet, we automatically construct a semantic knowledge
base called DisDict, which provides refined feature words that highlight the
differences among word senses, i.e., synsets. Second, we automatically generate
new sense-labeled data by DisDict from unlabeled corpora. Third, these
generated data, together with manually labeled data and unlabeled data, are fed
to a neural framework conducting supervised and unsupervised learning jointly
to model the semantic relations among synsets, feature words and their
contexts. The experimental results show that KDSL outperforms several
representative state-of-the-art methods on various major benchmarks.
Interestingly, it performs relatively well even when manually labeled data is
unavailable, thus provides a potential solution for similar tasks in a lack of
manual annotations.
