Approaches for Word Sense Disambiguation – A Survey
Pranjal Protim Borah1, Gitimoni Talukdar2, Arup Baruah3
1Pranjal Protim Borah, Department of Computer Science and Engineering, Assam Don Bosco University, Guwahati (Assam), India.
2Gitimoni Talukdar, Department of Computer Science and Engineering, Assam Don Bosco University, Guwahati (Assam), India.
3Arup Baruah, Assistant Professor, Department of Computer Science and Engineering, Assam Don Bosco University, Guwahati (Assam), India.
Manuscript received on 20 March 2014 | Revised Manuscript received on 25 March 2014 | Manuscript published on 30 March 2014 | PP: 135-138 | Volume-3 Issue-1, March 2014 | Retrieval Number: A1020033114/2014©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Word sense disambiguation is a technique in the field of natural language processing where the main task is to find the correct sense in which a word occurs in a particular context. It is found to be of vital help to applications such as question answering, machine translation, text summarization, text classification, information retrieval etc. This has resulted in excessive interest in approaches based on machine learning which performs classification of word senses automatically. The main motivation behind word sense disambiguation is to allow the users to make ample use of the available technologies because ambiguities present in any language provide great difficulty in the use of information technology as words in human language that occur in a particular context can be interpreted in more than one way depending on the context. In this paper we put forward a survey of supervised, unsupervised and knowledge based approaches and algorithms available in word sense disambiguation (WSD).
Keywords: Machine readable dictionary, Machine translation, Natural language processing, Wordnet, Word sense disambiguation.
Scope of the Article: Natural language Processing