CFP last date
20 May 2024
Reseach Article

Statistical Measure to Compute the Similarity between Answers in Online Question-Answering Portal

by Shashank, Shailendra Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 15
Year of Publication: 2014
Authors: Shashank, Shailendra Singh
10.5120/18154-9437

Shashank, Shailendra Singh . Statistical Measure to Compute the Similarity between Answers in Online Question-Answering Portal. International Journal of Computer Applications. 103, 15 ( October 2014), 35-39. DOI=10.5120/18154-9437

@article{ 10.5120/18154-9437,
author = { Shashank, Shailendra Singh },
title = { Statistical Measure to Compute the Similarity between Answers in Online Question-Answering Portal },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 15 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 35-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number15/18154-9437/ },
doi = { 10.5120/18154-9437 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:34:41.233539+05:30
%A Shashank
%A Shailendra Singh
%T Statistical Measure to Compute the Similarity between Answers in Online Question-Answering Portal
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 15
%P 35-39
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sentence similarity plays an important role in the field of natural language processing where it can be used for information processing, text mining and online question answering portals. Among all these applications sentence similarity is one of the most critical issues which has attracted the attention of many researchers. In this paper, a system which automates the process of subjective answer checking with high accuracy is proposed. Answer assessment system is an arrangement where answers given by users are matched with the stored answers in question answer database to judge their correctness. The main focus here is to improve the similarity of user's answer with the stored answer in question answer database. In addition to this, the intention is to remove the mistakes from answer checking process through automated system. For experimental purpose a set of questions and their answers have been taken and these questions have been answered by a random user. Results show that the method gives higher accuracy as compared to original method. Proposed method makes question answering process fast and efficient.

References
  1. F. Mandreoli, R. Emilia, R. Martoglia, and P. Tiberio, "A Syntactic Approach for Searching Similarities within Sentences," in Proceedings of eleventh international conference on information and knowledge management, 2002, pp. 635–637.
  2. J. Allan, C. Wade, and A. Bolivar, "Retrieval and novelty detection at the sentence level," in Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval - SIGIR '03, 2003, pp. 314–321.
  3. Y. Zhang, L. Gong, and Y. Wang, "An improved TF-IDF approach for text classification," J. Zhejiang Univ. Sci. , vol. 6, no. 1, pp. 49–55, Jan. 2005.
  4. Y. Li, D. McLean, Z. a. Bandar, J. D. O'Shea, and K. Crockett, "Sentence similarity based on semantic nets and corpus statistics," IEEE Trans. Knowl. Data Eng. , vol. 18, no. 8, pp. 1138–1150, Aug. 2006.
  5. P. Achananuparp, X. Hu, and S. Xiajiong, "The Evaluation of Sentence Similarity Measures," in Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery (DaWaK 08), 2008, vol. 5182, pp. 305–316.
  6. D. Metzler, S. Dumais, and C. Meek, "Similarity Measures for Short Segments of Text," in Proceedings of the 29th European conference on IR research ECIR 07, 2007, pp. 16–27.
  7. S. Banerjee and T. Pedersen, "Extended Gloss Overlaps as a Measure of Semantic Relatedness," in Proceedings of the 18th international joint conference on Artificial intelligence IJCAI03, 2003, pp. 805–810.
  8. H. Dong, J. Wu, X. Zhao, and Y. Li, "Study on the Calculation of Text Similarity Based on Key-sentence," in 2010 International Conference on E-Business and E-Government, 2010, pp. 1952–1955.
  9. D. Wang, "Improved sentence similarity algorithm based on VSM and its applications in question answering system," in Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference, 2010, pp. 368–371.
  10. Z. M. Juan, "An Effective Similarity Measurement for FAQ Question Answering System," in 2010 International Conference on Electrical and Control Engineering, 2010, pp. 4638–4641.
  11. D. Metzler, Y. Bernstein, W. B. Croft, A. Moffat, and J. Zobel, "Similarity measures for tracking information flow," in Proceedings of the 14th ACM international conference on Information and knowledge management - CIKM '05, 2005, pp. 517–524.
  12. A. Islam and D. Inkpen, "Semantic text similarity using corpus-based word similarity and string similarity," ACM Trans. Knowl. Discov. Data, vol. 2, no. 2, pp. 1–25, Jul. 2008.
  13. J. Zhang, Y. Sun, H. Wang, and Y. He, "Calculating Statistical Similarity between Sentences," J. Converg. Inf. Technol. , vol. 6, no. 2, pp. 22–34, 2011.
  14. S. Chauhan, P. Arora, and P. Bhadana, "Algorithm for Semantic Based Similarity Measure," Int. J. Eng. Sci. Invent. , vol. 2, no. 6, pp. 75–78, 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Answer assessment system Statistical similarity sentence and Question answering portal et al.