In Word Sense Disambiguation (WSD), the predominant approach generally involves a supervised system trained on sense annotated corpora. The limited quantity of such corpora however restricts the coverage and the performance of these systems. In this article, we propose a new method that solves these issues by taking advantage of the knowledge present in WordNet, and especially the hypernymy and hyponymy relationships between synsets, in order to reduce the number of different sense tags that are necessary to disambiguate all words of the lexical database. Our method leads to state of the art results on most WSD evaluation tasks, while improving the coverage of supervised systems, reducing the training time and the size of the models, without additional training data. In addition, we exhibit results that significantly outperform the state of the art when our method is combined with an ensembling technique and the addition of the WordNet Gloss Tagged as training corpus.

本文提出了一种新方法解决词义消歧中受语料库覆盖率和效率限制的问题，利用WordNet中的知识以及Synsets之间的上下位关系来减少所需的不同词义标记的数量，从而在大多数WSD评估任务上实现最先进的结果，在不使用附加训练数据的情况下提高督导系统的覆盖范围、减少训练时间和模型的大小，此方法结合集成技术和WordNet Gloss Tagged作为训练语料时可以得到显着的超越当前最先进状态的结果。

通过上下位关系提高神经词义消歧的覆盖率和泛化能力