Comparative Analysis of Supervised Approaches for Word Sense Disambiguation Using Text Similarity
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 6
Abstract
The words that are often being correspond to two or more meanings rather than to a single meaning results in semantically-ambiguous words . Measuring the similarity between words, sentences , paragraphs is an important part in information retrieval and word sense disambiguation tasks. One of the biggest challenges in Natural Language Processing is for the system to encompass in what sense a specific word is being used .This paper describes the analysis of text in order to a certain first the similarity in case that exists. Second the effort has been made to resolve the ambiguity in the text. The paper presents the comparison of machine learning approaches in the text similarity analysis. The Naive bayes approach was observed to outperform other approaches including SVM , Max Entropy , Tree , Random Forest and Bagging . Keywords: Text Similarity, Word Sense Disambiguation, Approaches, SENSEVAL, Supervised machine learning algorithms.
Authors and Affiliations
Subha Mahajan, Rakesh Kumar, Vibhakar Mansotra
Keywords
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