Word Sense Disambiguation (WSD) aims to identify the correct meaning of
polysemous words in the particular context. Lexical resources like WordNet
which are proved to be of great help for WSD in the knowledge-based methods.
However, previous neural networks for WSD always rely on massive labeled data
(context), ignoring lexical resources like glosses (sense definitions). In this
paper, we integrate the context and glosses of the target word into a unified
framework in order to make full use of both labeled data and lexical knowledge.
Therefore, we propose GAS: a gloss-augmented WSD neural network which jointly
encodes the context and glosses of the target word. GAS models the semantic
relationship between the context and the gloss in an improved memory network
framework, which breaks the barriers of the previous supervised methods and
knowledge-based methods. We further extend the original gloss of word sense via
its semantic relations in WordNet to enrich the gloss information. The
experimental results show that our model outperforms the state-of-theart
systems on several English all-words WSD datasets.

该论文提出了一种新的神经网络模型 GAS，在词汇资源和上下文信息的共同作用下实现了更好的词义消歧效果。