A Comparative Study of Deep Learning Models for Word-Sense Disambiguation

Abstract

Word-sense disambiguation (WSD) has been a persistent issue since its introduction to the community of natural language processing (NLP). It has a wide range of applications in different areas like information retrieval (IR), sentiment analysis, knowledge graph construction, machine translation, lexicography, text mining, information extraction, and so on. Analysis of the performance of deep learning algorithms with different word embeddings is required to be done since various deep learning models are deployed for the task of disambiguation of word sense. In this paper, comparison of several deep learning models like CNN, LSTM, bidirectional LSTM, and CNN + LSTM is done with trainable as well as pretrained GloVe embeddings with common preprocessing methods. Performance evaluation of temporal convolutional network (TCN) model is done along with the comparison of the same with the formerly mentioned models. This paper shows that using GloVe embeddings may not result in better accuracy in the case of word-sense disambiguation, i.e., trainable embeddings perform better. It also includes a framework for evaluating deep learning models for WSD and analysis of the usage of embeddings for the same. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.