Question Classification using Naive Bayes Classifier and Creating Missing Classes using Semantic Similarity in Question Answering System

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2015 by IJETT Journal
Volume-23 Number-4
Year of Publication : 2015
Authors : Jeena Mathew, Shine N Das
  10.14445/22315381/IJETT-V23P231

MLA 

Jeena Mathew, Shine N Das"Question Classification using Naive Bayes Classifier and Creating Missing Classes using Semantic Similarity in Question Answering System", International Journal of Engineering Trends and Technology (IJETT), V23(4),155-160 May 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Question Classification is the core component of the Question Answering System. The quality of the question answering system depends on the results of the question classification. Almost all the question classification algorithms are based on the classes defined by Li and Roth [2].In this paper, a question classification algorithm based on Naïve Bayes Classifier and question semantic similarity is proposed. This paper mainly focuses on Numeric and Location type questions. Naive Bayes Classifier is adopted to classify the questions into Numeric and Location classes and semantic similarity is used to classify the questions into their fine-grained classes. According to Li and Roth, the coarse grained class Numeric and Location has fine-grained class Other. In this paper, we also present the method to replace the Other class in Numeric and Location classes by creating new classes and adding the newly created classes in the hierarchy.

 References

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Keywords
Naïve Bayes Classifier, Natural Language Processing, Question Answering, Question Class Hierarchy, Question Classification, Semantic Similarity.