Word sense disambiguation: Mathematical modelling of adaptive word embedding technique for word vector

Kokane, Chandrakant D.; Babar, Sachin D.; Mahalle, Parikshit Narendra; Patil, Shivprasad P.

doi:10.47974/JIM-1675

Open Access Research Article

Word sense disambiguation: Mathematical modelling of adaptive word embedding technique for word vector

^*Chandrakant D. KokaneCorresponding authorcdkoakne1992@gmail.comDepartment of Computer Engineering Savitribai Phule Pune University Smt. Kashibai Navale College of EngineeringPune, Maharashtra, IndiaView full profile → , Sachin D. Babarsdbabar@sinhgad.eduDepartment of Computer Engineering Savitribai Phule Pune University Smt. Kashibai Navale College of EngineeringPune, Maharashtra, IndiaView full profile → , Parikshit Narendra Mahalleaalborg.pnm@gmail.comDepartment of Artificial Intelligence and Data Science Vishwakarma Institute of Information TechnologyDepartment of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology SPPU Pune Maharashtra IndiaPune, Maharashtra, IndiaView full profile → , Shivprasad P. Patilprincipal.nbnsscoe@sinhgad.eduSavitribai Phule Pune University NBN Sinhgad School of EngineeringPune, Maharashtra, IndiaView full profile →

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pp. 475–482Vol. 26Issue 3April 2023DOI: 10.47974/JIM-1675XML

Published Online:: 01 Apr 2023
Article type:: Research Article
Language:: EN
Article no.:: JIM-1675
Pages:: 475–482

Abstract

Word embedding is the method of representing ambiguous words into word vectors. The existing methods of word embedding are applicable for homonymous words. Constructing word vector of polysemous words is the challenge. The word vector of polysemous words are made by considering context information. The proposed adaptive word embedding technique is discussed in this article. The adaptive word embedding technique is applicable for both homosemous and polysemous words. While representing ambiguous word into word vector the context information is considered. The adaptive word embedding technique generates dynamic word vector for ambiguous word. The word vector with dimension size 198 is created here. There are 198 features are considered in the discussed model. The countable nouns are used as features in adaptive word embedding.

Keywords

Context information Semantic similarity Polysemous words Adaptive word embedding

Subject Classifications

Primary 93A30Secondary 49K15

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