  State-of-the-art methods for Word Sense Disambiguation (WSD) combine two
different features: the power of pre-trained language models and a propagation
method to extend the coverage of such models. This propagation is needed as
current sense-annotated corpora lack coverage of many instances in the
underlying sense inventory (usually WordNet). At the same time, unambiguous
words make for a large portion of all words in WordNet, while being poorly
covered in existing sense-annotated corpora. In this paper, we propose a simple
method to provide annotations for most unambiguous words in a large corpus. We
introduce the UWA (Unambiguous Word Annotations) dataset and show how a
state-of-the-art propagation-based model can use it to extend the coverage and
quality of its word sense embeddings by a significant margin, improving on its
original results on WSD.
