A Literature Survey on Word Sense Disambiguation for the Hindi Language

Abstract

Word sense disambiguation (WSD) is a process used to determine the most appropriate meaning of a word in a given contextual framework, particularly when the word is ambiguous. While WSD has been extensively studied for English, it remains a challenging problem for resource-scarce languages such as Hindi. Therefore, it is crucial to address ambiguity in Hindi to effectively and efficiently utilize it on the web for various applications such as machine translation, information retrieval, etc. The rich linguistic structure of Hindi, characterized by complex morphological variations and syntactic nuances, presents unique challenges in accurately determining the intended sense of a word within a given context. This review paper presents an overview of different approaches employed to resolve the ambiguity of Hindi words, including supervised, unsupervised, and knowledge-based methods. Additionally, the paper discusses applications, identifies open problems, presents conclusions, and suggests future research directions.

Preface to the Special Issue on Computational Linguistics and Natural Language Processing

“…The paper "A Literature Survey on Word Sense Disambiguation for the Hindi Language" by Gujjar et al [7] focuses on the process to determine the exact context-specific meanings of ambiguous words, for example, the English word bark, which can mean the sound emitted by dogs, the outer sheath of a tree trunk, or a kind of ship. While many natural language processing techniques were developed to deal with the disambiguation of English words, the disambiguation of Hindi words sometimes requires language-specific algorithms that are reviewed in this paper.…”