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A Survey on Supervised Learning for Word Sense Disambiguation
ABHISHEK FULMARI, MANOJ B. CHANDAK Student M.Tech, CSE Department, Shri Ramdeobaba College of Engineering and Management, Nagpur, India Professor, CSE Department, Shri Ramdeobaba College of Engineering and Management, Nagpur, India
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Abstract: Word Sense Disambiguation (WSD) is the process of determining which sense of a word is used in a given context. Due to its importance in understanding semantics it is used in many real-world applications like web information retrieval, machine translation and information extraction. The problem of WSD is mainly considered as AI- complete problem. This paper discussed supervised approach, unsupervised approach, NaΓ―ve Bayes method, Exemplar based learning method, Decision List method for WSD.
Keywords: Word Sense Disambiguation, Supervised Approach, NaΓ―ve Bayes Methods, Exemplar-based Learning Methods, Unsupervised Approach.
Keywords: Word Sense Disambiguation, Supervised Approach, NaΓ―ve Bayes Methods, Exemplar-based Learning Methods, Unsupervised Approach.
How to Cite:
[1] ABHISHEK FULMARI, MANOJ B. CHANDAK Student M.Tech, CSE Department, Shri Ramdeobaba College of Engineering and Management, Nagpur, India Professor, CSE Department, Shri Ramdeobaba College of Engineering and Management, Nagpur, India, βA Survey on Supervised Learning for Word Sense Disambiguation,β International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE)
This work is licensed under a Creative Commons Attribution 4.0 International License.