  As a key natural language processing (NLP) task, word sense disambiguation
(WSD) evaluates how well NLP models can understand the lexical semantics of
words under specific contexts. Benefited from the large-scale annotation,
current WSD systems have achieved impressive performances in English by
combining supervised learning with lexical knowledge. However, such success is
hard to be replicated in other languages, where we only have limited
annotations.In this paper, based on the multilingual lexicon BabelNet
describing the same set of concepts across languages, we propose building
knowledge and supervised-based Multilingual Word Sense Disambiguation (MWSD)
systems. We build unified sense representations for multiple languages and
address the annotation scarcity problem for MWSD by transferring annotations
from rich-sourced languages to poorer ones. With the unified sense
representations, annotations from multiple languages can be jointly trained to
benefit the MWSD tasks. Evaluations of SemEval-13 and SemEval-15 datasets
demonstrate the effectiveness of our methodology.
