Information Retrieval: Improving Question Answering Systems by Query Reformulation and Answer Validation
Question answering (QA) aims at retrieving precise information from a large collection of documents. Most of the Question Answering systems composed of three main modules: question processing, document processing and answer processing. Question processing module plays an important role in QA systems to reformulate questions. Moreover answer processing module is an emerging topic in QA systems, where these systems are often required to rank and validate candidate answers. These techniques aiming at finding short and precise answers are often based on the semantic relations and co-occurrence keywords. This paper discussed about a new model for question answering which improved two main modules, question processing and answer processing which both affect on the evaluation of the system operations. There are two important components which are the bases of the question processing. First component is question classification that specifies types of question and answer. Second one is reformulation which converts the user's question into an understandable question by QA system in a specific domain. The objective of an Answer Validation task is thus to judge the correctness of an answer returned by a QA system, according to the text snippet given to support it. For validating answers we apply candidate answer filtering, candidate answer ranking and also it has a final validation section by user voting. Also this paper described new architecture of question and answer processing modules with modeling, implementing and evaluating the system. The system differs from most question answering systems in its answer validation model. This module makes it more suitable to find exact answer. Results show that, from total 50 asked questions, evaluation of the model, show 92% improving the decision of the system.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081840Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2413
 Demner-Fushman, Dina, "Complex Question Answering Based on Semantic Domain Model of Clinical Medicine", OCLC's Experimental Thesis Catalog, College Park, Md.: University of Maryland (United States), 2006.
 Doan-Nguyen Hai, Leila Kosseim, "The Problem of Precision in Restricted-Domain Question Answering. Some Proposed Methods of Improvement", In Proceedings of the ACL 2004 Workshop on Question Answering in Restricted Domains, Barcelona, Spain, Publisher of Association for Computational Linguistics, July 2004, PP.8-15.
 Green, W., Chomky, C., Laugherty, K. BASEBALL: "An automatic question answer". Proceeding of the western Joint Computer Conference, 1961, PP. 219-224.
 Figueira, H. Martins, A. Mendes, A. Mendes, P. Pinto, C. Vidal, D ,"Priberam's Question Answering System in a Cross- Language Environment",LECTURE NOTES IN COMPUTER SCIENCE, Volume 4730, 2007,PP. 300-309.
 Dan Moldovan, Sanda Harabagiu, Marius Pasca, Roxana Girgu, " The Structure and Performance of an Open-domain Question Answering System", Proceedings of the 38th Annual Meeting on Association for Computational Linguistics Hon Kong, 2000, PP. 563-570,.
 Cody Kwok, Oren Etzioni, Daniel S. Weld, "Scaling Question Answering to the Web", Proceedings of the 10th international conference on World Wide Web, Hong Kong , 2001,PP. 150-161.
 Maria Varges, Verona and Enrico Motta, "AQUA, A Knowledge-Based Architecture for a Question Answering System", Tech Report Kmi-o4-15, Knowledge media institute Milton Keynes, England, 2004.
 Lehnert, W. G. "A conceptual theory of question answering". In International Joint Conference on Artificial Intelligence (IJCAI 1977), 1977, PP. 158-164.
 Garg, A. X.; Adhikari, N. K. J.; McDonald, H.; Rosas- Arellano, M. P.; Devereaux,P. J.; Beyene, J.; Sam, J.; and Haynes, R. B. E.ects of "computerized clinical decision support systems on practitioner performance and patient outcomes". The Journal of the American Medical Association 293(10), 2005, pp.1223-1238.
 Alexander Panossian, Georg Wikman , "Knowledge Bases in Medicine: a review". Journal of Ethno pharmacology, Bulletin of the Medical Library Association ,Volume 118, Issue 2, 23 July 2007, PP. 183-212.
 Magnini, B., Negri, M., Prevete, R., Tanev, H.: "Comparing Statistical and Content-Based Techniques for Answer Validation on the Web", Proceedings of the VIII Convegno AI*IA, Siena, Italy, 2002.
 Magnini, B., Negri, M., Prevete, R., Tanev, H.: "Is It the Right Answer? Exploiting Web Redundancy for Answer Validation", Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL-2002), Philadelphia, PA. 2002.
 Magnini, B., Negri, M., Prevete, R., Tanev, H.: "A WordNet-Based Approach to Named Entities Recognition", Proceedings of SemaNet02, COLING Workshop on Building and Using Semantic Networks, Taipei, Taiwan, 2002.
 Hai Doan-Nguyen, Leila Kosseim: "Improving the Precision of a Closed-Domain Question-Answering System with Semantic Information", ACL 2004 Workshop on Question Answering in Restricted Domain,2004- acl.ldc.upenn.edu
 Fellbaum, "WordNet: an Electronic Lexical Database". The MIT Press , 1998