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Title:Question Answering using Vector Based Information Retrieval - Paradigm with Word Sense Disambiguation
Author(s):Ganesan, Kavita
Subject(s):information retrieval, question answering
Abstract:Vector Based Text Classification in the Question Answering field has long been explored. However, there has not been any attempt so far to take word senses into consideration in the development of the feature sets in the classifier. This paper aims to investigate the performance of a question answering text classifier built using not just the root form of words but also taking the senses of those words into thought. Having done a 10-folded cross validation, the classification error rate using the tri-gram model actually shows that there is a significant improvement when the sense of a word is actually known. This shows that the usage of word sense in building the classifier has indeed a strong association with the classification accuracy.
Issue Date:2006
Genre:Technical Report
Type:Text
Language:English
URI:http://hdl.handle.net/2142/15278
Publication Status:unpublished
Date Available in IDEALS:2010-03-30


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