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
this http URL 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.

本文提出了一种基于多语言词典建立知识和监督为基础的多语言词义消歧（MWSD）系统的方法，利用统一的语义表示，将多种语言的注释进行共同训练以解决 MWSD 的注释稀缺问题，并在 SemEval-13 和 SemEval-15 数据集上展示了该方法的有效性。