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

Improving the Coverage and the Generalization Ability of Neural Word  Sense Disambiguation through Hypernymy and Hyponymy Relationships

Lexical entailment, such as hyponymy, is a fundamental issue in the semantics
of natural language. This paper proposes distributional semantic models which
efficiently learn word embeddings for entailment, using a recently-proposed
framework for modelling entailment in a vector-space. These models postulate a
latent vector for a pseudo-phrase containing two neighbouring word vectors. We
investigate both modelling words as the evidence they contribute about this
phrase vector, or as the posterior distribution of a one-word phrase vector,
and find that the posterior vectors perform better. The resulting word
embeddings outperform the best previous results on predicting hyponymy between
words, in unsupervised and semi-supervised experiments.

该研究提出了一种基于向量空间的框架来对词汇包容性建模的分布语义模型，利用伪短语中邻近的两个词向量提出了一种隐向量，并探究了模拟词作为证据或词作为后验分布的优缺点。实验表明，所得到的词嵌入模型优于之前预测词之间下义词关系的最佳结果。