论文标题
结合文字诱导的神经语言模型
Combining Neural Language Models for WordSense Induction
论文作者
论文摘要
单词感应诱导（WSI）是根据该词的表达含义对歧义单词进行分组的问题。最近，提出了一种新的方法，该方法使用神经语言模型在特定上下文中生成了模棱两可的单词的可能替代品，然后将稀疏的单词袋矢量构成。在这项工作中，我们将这种方法应用于俄罗斯语言，并通过两种方式进行改进。首先，我们提出了结合左和右上下文的方法，从而产生了更好的替代品。其次，而不是为所有模棱两可的单词固定数量的簇，我们提出了一种用于为每个单词选择单个簇数的技术。我们的方法建立了新的最新水平，从而提高了俄罗斯语言在两个Russe 2018数据集中的WSI的最佳结果。
Word sense induction (WSI) is the problem of grouping occurrences of an ambiguous word according to the expressed sense of this word. Recently a new approach to this task was proposed, which generates possible substitutes for the ambiguous word in a particular context using neural language models, and then clusters sparse bag-of-words vectors built from these substitutes. In this work, we apply this approach to the Russian language and improve it in two ways. First, we propose methods of combining left and right contexts, resulting in better substitutes generated. Second, instead of fixed number of clusters for all ambiguous words we propose a technique for selecting individual number of clusters for each word. Our approach established new state-of-the-art level, improving current best results of WSI for the Russian language on two RUSSE 2018 datasets by a large margin.
