Combining Neural Language Models for WordSense Induction

TL;DR

This paper enhances neural language model-based word sense induction for Russian by combining context directions and customizing cluster numbers, achieving state-of-the-art results on RUSSE 2018 datasets.

Contribution

It introduces methods for combining left and right contexts and selecting individual cluster counts, significantly improving WSI performance for Russian.

Findings

Achieved new state-of-the-art results on RUSSE 2018 datasets.

Improved substitute generation by combining context directions.

Customized clustering per word enhances sense induction accuracy.

Abstract

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.