  A promising approach for knowledge-based Word Sense Disambiguation (WSD) is
to select the sense whose contextualized embeddings computed for its definition
sentence are closest to those computed for a target word in a given sentence.
This approach relies on the similarity of the \textit{sense} and
\textit{context} embeddings computed by a pre-trained language model. We
propose a semantic specialization for WSD where contextualized embeddings are
adapted to the WSD task using solely lexical knowledge. The key idea is, for a
given sense, to bring semantically related senses and contexts closer and send
different/unrelated senses farther away. We realize this idea as the joint
optimization of the Attract-Repel objective for sense pairs and the
self-training objective for context-sense pairs while controlling deviations
from the original embeddings. The proposed method outperformed previous studies
that adapt contextualized embeddings. It achieved state-of-the-art performance
on knowledge-based WSD when combined with the reranking heuristic that uses the
sense inventory. We found that the similarity characteristics of specialized
embeddings conform to the key idea. We also found that the (dis)similarity of
embeddings between the related/different/unrelated senses correlates well with
the performance of WSD.
