A Hybrid Approach to Word Sense Disambiguation Combining Supervised and Unsupervised Learning

TL;DR

This paper presents a hybrid Word Sense Disambiguation method combining supervised and unsupervised learning, using enriched dynamic training data and a mixed approach to improve accuracy over traditional methods.

Contribution

It introduces a novel hybrid methodology integrating Modified Lesk and Bag-of-Words approaches with dynamic data enrichment for better disambiguation.

Findings

Outperforms individual Modified Lesk and Bag-of-Words methods

Uses dynamic learning set enrichment for improved training data

Demonstrates superior accuracy through experimentation

Abstract

In this paper, we are going to find meaning of words based on distinct situations. Word Sense Disambiguation is used to find meaning of words based on live contexts using supervised and unsupervised approaches. Unsupervised approaches use online dictionary for learning, and supervised approaches use manual learning sets. Hand tagged data are populated which might not be effective and sufficient for learning procedure. This limitation of information is main flaw of the supervised approach. Our proposed approach focuses to overcome the limitation using learning set which is enriched in dynamic way maintaining new data. Trivial filtering method is utilized to achieve appropriate training data. We introduce a mixed methodology having Modified Lesk approach and Bag-of-Words having enriched bags using learning methods. Our approach establishes the superiority over individual Modified Lesk and Bag-of-Words approaches based on experimentation.