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Reseach Article

Question Answering System based on Question Classification and Sentential Level Ranking

by Shruti Gupta, Shilpi Malhotra
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 93 - Number 15
Year of Publication: 2014
Authors: Shruti Gupta, Shilpi Malhotra
10.5120/16291-5895

Shruti Gupta, Shilpi Malhotra . Question Answering System based on Question Classification and Sentential Level Ranking. International Journal of Computer Applications. 93, 15 ( May 2014), 13-18. DOI=10.5120/16291-5895

@article{ 10.5120/16291-5895,
author = { Shruti Gupta, Shilpi Malhotra },
title = { Question Answering System based on Question Classification and Sentential Level Ranking },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 93 },
number = { 15 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 13-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume93/number15/16291-5895/ },
doi = { 10.5120/16291-5895 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:15:49.486472+05:30
%A Shruti Gupta
%A Shilpi Malhotra
%T Question Answering System based on Question Classification and Sentential Level Ranking
%J International Journal of Computer Applications
%@ 0975-8887
%V 93
%N 15
%P 13-18
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Question answering system provides the way which helps us in reducing the time of search for the useful information from huge amount of data. An extensive work has been done in the field of Question Answering systems but there exists a scope of further improvement in this field. In the proposed architecture the answers are extracted by correctly classifying the questions. Paragraph ranking is used to reduce the text that reduces the memory as well as processing requirement. Named Entity Recognition technique helps to increase the accuracy of the answer returned. The proposed implementation takes question as input from user, classifies the question and then attempts to find answer that will be based on corresponding answer type. The techniques like paragraph ranking, preprocessing, indexing etc are used to improve the efficiency and accuracy of the system. Thus the system provides accurate and relevant answer of the query without making so much effort and also helps in reducing overall time in searching for answers. Moreover the result returned will be very concise.

References
  1. ciir. cs. umass. edu irchallenges presentations summari ation3. doc
  2. Ani Nenkova, Kathleen McKeown "Automatic Summarization" , Foundations and TrendsR in Information Retrieval Vol. 5, Nos. 2–3 (2011) 103–233
  3. Terrence A. Brooks
  4. Web Search: How the Web has changed information retrieval, Information Research.
  5. Dell Zhang, Wee Sun Lee
  6. "Question Classification using Support Vector Machines",in proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval.
  7. David Pinto, Michael Branstein, Ryan Coleman, W. Bruce Croft, Matthew King, Wei Li and Xing Wei
  8. QuASM: A System for Question Answering Using Semi-Structured Data, 2nd ACM/IEEE-CS joint conference on Digital Libraries, Amherst, MA.
  9. Mani, I. , MayBury, "M. T.
  10. Advances in Automatic Text Summarization", the MIT Press.
  11. Renu Mudgal, Rosy Madaan, A. K. Sharma , Ashutosh Dixit , "A Novel Architecture for Question Classification Based Indexing Scheme for Efficient Question Answering", International Journal of Computer Engineering & Applications, Vol. II, Issue II
  12. Shilpi Mahlotra,"Web Document Summarization Using Multiple Document Reference," MIT International Journal of Computer Science & Information Technology
  13. Rosy Madaan, A. K. Sharma, Ashutosh Dixit
  14. "A Novel Architecture for a Blog Crawler", 2nd IEEE International Conference on Parallel Distributed and Grid Computing (PDGC).
  15. Rafeeq Al-Hashemi, "Text Summari ation Extraction System (TSES) Using Extracted Keywords". International Arab Journal of e-Technology, Vol. 1, No. 4, June 2010.
  16. http://www. cs. ccsu. edu/~markov/ccsu_courses/DataMining-3. html
  17. http://en. wikipedia. org/wiki/Precision_and_recall
Index Terms

Computer Science
Information Sciences

Keywords

Indexing Named Entity Recognition Question Classification Ranking Summarization