WSD for Assamese Language

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 740)


Word sense ambiguity comes about the use of lexemes associated with more than one sense. In this research work, an improvement has been proposed and evaluated for our previously developed Assamese Word-Sense Disambiguation (WSD) system where potential outcomes of using semantic features were evaluated up to a limited extent. As semantic relationship information has a good effect in most of the natural language processing (NLP) tasks, in this work, the system is developed based on supervised learning approach using Naïve Bayes classifier with syntactic as well as semantic features. The performance measure of the overall system has been improved up to 91.11% in terms of F1-measure as compared to 86% of the previously developed system by incorporating the Semantically Related Words (SRW) feature in our feature set.


Word sense ambiguity Naïve Bayes classifier Semantic feature Corpus Prior probability 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of DesignIndian Institute of Technology GuwahatiGuwahatiIndia
  2. 2.Department of Computer Science and EngineeringRoyal Group of InstitutionsGuwahatiIndia
  3. 3.Department of Computer Science and EngineeringAssam Don Bosco UniversityGuwahatiIndia

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