Automatic classification of bengali sentences based on sense definitions present in bengali wordnet

TL;DR

This paper presents an automatic method for classifying Bengali sentences into senses using Bengali WordNet and Naive Bayes, achieving 84% accuracy and aiding in NLP tasks like text classification and word sense disambiguation.

Contribution

It introduces a novel approach for Bengali sentence sense classification leveraging WordNet and probabilistic modeling, with promising experimental results.

Findings

Achieved 84% accuracy in sense classification

Identified syntactic and semantic issues affecting classification

Demonstrated applicability in NLP tasks like text classification

Abstract

Based on the sense definition of words available in the Bengali WordNet, an attempt is made to classify the Bengali sentences automatically into different groups in accordance with their underlying senses. The input sentences are collected from 50 different categories of the Bengali text corpus developed in the TDIL project of the Govt. of India, while information about the different senses of particular ambiguous lexical item is collected from Bengali WordNet. In an experimental basis we have used Naive Bayes probabilistic model as a useful classifier of sentences. We have applied the algorithm over 1747 sentences that contain a particular Bengali lexical item which, because of its ambiguous nature, is able to trigger different senses that render sentences in different meanings. In our experiment we have achieved around 84% accurate result on the sense classification over the total input sentences. We have analyzed those residual sentences that did not comply with our experiment and did affect the results to note that in many cases, wrong syntactic structures and less semantic information are the main hurdles in semantic classification of sentences. The applicational relevance of this study is attested in automatic text classification, machine learning, information extraction, and word sense disambiguation.