Research Article
Exploiting Synonyms to Improve Question and Answering Systems
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 108 - Issue 18 |
| Published: December 2014 |
| Authors: Anietie Andy, Mugizi Robert, Mohamed Chouikha |
10.5120/19012-0523
|
Anietie Andy, Mugizi Robert, Mohamed Chouikha . Exploiting Synonyms to Improve Question and Answering Systems. International Journal of Computer Applications. 108, 18 (December 2014), 24-27. DOI=10.5120/19012-0523
@article{ 10.5120/19012-0523,
author = { Anietie Andy,Mugizi Robert,Mohamed Chouikha },
title = { Exploiting Synonyms to Improve Question and Answering Systems },
journal = { International Journal of Computer Applications },
year = { 2014 },
volume = { 108 },
number = { 18 },
pages = { 24-27 },
doi = { 10.5120/19012-0523 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2014
%A Anietie Andy
%A Mugizi Robert
%A Mohamed Chouikha
%T Exploiting Synonyms to Improve Question and Answering Systems%T
%J International Journal of Computer Applications
%V 108
%N 18
%P 24-27
%R 10.5120/19012-0523
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Community Question and Answering (CQA) systems are a popular way for Internet users to get answers to complex and common everyday questions. One of the challenges with CQA is that some of the asked questions are not answered [8]. This paper addresses this challenge by using a synonym-based approach that expands each unanswered question into several related questions. This paper argues that the number of unanswered questions can be reduced by searching the data set for the most similar resolved question(s) (questions that have been satisfactorily answered) to either the unanswered question and / or any of its expanded questions. If this search returns more than one resolved question, we rank the returned questions and choose the highest ranking resolved question as the most similar to the unanswered question.
References
- Salton, G. and McGill, M, 1987. Introduction to Modern Information Retrieval. McGraw-Hill, NY.
- Similarity Measures for Text Document Clustering A. Huang, University of Waikato, Hamilton, New Zealand
- Rao, D. , McNamee P. , and Dredze M. 2010 Entity Linking: Finding Extracted Entities in a Knowledge Base
- Gyongyi, Z. , Koutrika, G, Pederson, J, and Garcia-Molina, Hector. Questioning Yahoo! Answers.
- Lopez, V, Unger, Christina, Cimiano, P, Motta, Enrico. Evaluating Question Answering over Linked Data.
- Zhou, T, Si, X, Chang, E, King, I, and Lyu, M. A Data driven approach to Question subjectivity identification in community question answering.
- Yih, W, Chang, M, Meek, C, Pastusiak, A. Question Answering Using enhanced Lexical Semantic Models.
- Dror, G, Koren, Y, Maarek, Y, Szpektor, I want to Answer, Who has a question? Yahoo! Answers Recommender System.
- Shtok, A, Dror, G, Maarek, Y, Szpektor, I. Learning from the Past: Answering New Questions with Pas
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