Semi-supervised Word Sense Disambiguation Using Example Similarity Graph
Semi-supervised Word Sense Disambiguation Using Example Similarity Graph
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DOI：
10.18653/v1/2020.textgraphs-1.6
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Rie Yatabe;Minoru Sasaki
中科院分区：
文献类型：
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作者：
Rie Yatabe;Minoru Sasaki
词义消歧是自然语言处理中的一个众所周知的问题。近年来，应用神经网络和机器学习技术来解决WSD问题越来越受到人们的关注。然而，这些先前的有监督的方法经常受到缺乏手动意义标记的示例的影响。为了解决这些问题，本文提出了一种基于图嵌入学习方法的半监督WSD方法，以有效利用已标记和未标记的样本。实验结果表明，该方法比以往的半监督WSD方法具有更好的分类效果。此外，实例之间的图结构对于词义分解是有效的，并且在所提出的方法中利用通过微调BERT得到的图结构是有效的。
Word Sense Disambiguation (WSD) is a well-known problem in the natural language processing. In recent years, there has been increasing interest in applying neural net-works and machine learning techniques to solve WSD problems. However, these previ-ous supervised approaches often suffer from the lack of manually sense-tagged exam-ples. In this paper, to solve these problems, we propose a semi-supervised WSD method using graph embeddings based learning method in order to make effective use of labeled and unlabeled examples. The results of the experiments show that the proposed method performs better than the previous semi-supervised WSD method. Moreover, the graph structure between examples is effective for WSD and it is effective to utilize a graph structure obtained by fine-tuning BERT in the proposed method.