Optimizing word sense disambiguation for Hindi language using extended Lesk and conceptual density
Lalita Kumari, Swarun Kumar · IET conference proceedings. · 2023
Word Sense Disambiguation (WSD) is a crucial task in natural language processing, aiming to determine the correct sense of a word in a given context. Word sense disambiguation (WSD) is an indispensable task in natural language processing for language understanding like question answering, information retrieval and machine translation system. Word sense disambiguation technique is to find most relevant sense of ambiguous word. In simple way WSD is a technique which clubs a word which has multiple meaning. Word Sense Disambiguation is an open challenging problem for Natural Language Processing. We people can understand the sense and meaning of word in different context. But for system to decide the best and correct meaning from different word meaning system needs to be trained. This paper provides a extended Lesk and Conceptual Density (CD) approach together to remove word sense ambiguity. The density of overlapped word from context and sense bag is calculated. BLUE is used to evaluate the score. On the basis of score, system is able to judge the correct sense of word. The proposed approach not only addresses the challenges specific to Hindi language but also offers a general framework that can be applied to other morphologically rich languages.