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Framework for Improved Question Answering System

Affiliations

  • Computer Science and Engineering, Sathyabama University, Chennai - 600119, Tamil Nadu, India
  • Department of Computer Science and Engineering, TRP Engineering College, Trichy - 621105, Tamil Nadu, India

Abstract


Background/Objectives: The proposed frame work uses statistical based similarity measure for QAS, which gives short and exact answer to user query. The entire frame work is focusing similarity computation between question and answer pair. Methods/Statistical Analysis: The involvement of similarity computation between question and answer pair should be investigated in this work QAS. Statistical based similarity measure is an important issue as compared to similarity computation in many fields such as, Natural Language Processing, Information Retrieval, Ontology Mapping, Knowledge Acquisition and Question Answering System. Semantic web has been used to present the structure knowledge representation between questions and answer keywords. Ontology can be used as important proposal to enable semantic similarity between keywords present in the question and answer pairs. Findings: The proposed frame work investigate, how the statistical based similarity measure used in these system helps to improve the performance of the QA system for all Wh-questions. We find the keywords having most similar meaning will return the answer as the final answer. The best performance is achieved by extracting the relevant snippets information form Google search engine. The performance of the system will change as the retrieval of the document is increases beyond 15; this could indicate that restriction in the retrieval of document is helps to optimize the performance of the system. Application/Improvements: This frame work is applicable to all type of search engines which helps to finds most relevant answer to user questions. In future, we implement our frame work for questions having more than five keywords.

Keywords

Information Retrieval, Ontology, Question Answering System, Semantic Similarity.

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References


  • Lee JH, Kim MH. Information Retrieval based on conceptual distance in is - A hierarchy. Journal of Documentation.1993; 49(2):188–207.
  • Resnik P. Semantic similarity in taxonomy. An information- based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research. 1999; 11:95–130.
  • Budanitsky A, Hirst G. Evaluating WordNet-based measures of semantic distance. Computational Linguistics. 2006; 32(1):13–47.
  • Sanchez D, Batet D. Domain ontology learning from the web: An unsupervised, automatic and domain independent approach. 2009.
  • Cilibrasi RL, Vitanyi PMB. The Google similarity distance. IEEE Transactions on Knowledge and Data Engineering. 2006; 19(3):370–83.
  • Lanzenberger M, Sampson J, Kargl H, Wimmer M, Conroy C. Making ontology talk: Knowledge interoperability in the semantic web IEEE Intelligent Systems. 2008; 23:72–85.
  • Hirst G, St-Onge D. Lexical chains as representations of context for the detection and correction of malapropisms. C. Fellbaum (Ed). WordNet: An Electronic Lexical Database. MIT Press; 1995. p. 305–32.
  • Mihalcea R, Corley C, Strapparava C. Corpus-based and knowledge-based measures of text semantic similarity. Proceedings of AAAI 21st National Conference on Artificial Intelligence; Boston, MA. 2006. p. 775–80.
  • Lin D. An information-theoretic definition of similarity. Proc of Fifteenth International Conference on Machine Learning, ICML; Madison, Wisconsin, USA. 1998. p. 296–304.
  • Hughes T, Ramage D. Lexical semantic relatedness with random graph walks. Proceedings of Emprical Methods in Natural Language Proceesing; Prague, Czech Republic. 2007. p. 581–9.
  • Kaisser M. Question answering based on semantic roles. Proceedings of The Deep Linguistic Processing Workshop in 45th Annual Meeting of the Association for Computational Linguistics; 2007. p. 41–8.
  • Jarmasz M, Szpakowicz S. Roget’s thesaurus and semantic similarity. Proceedings of RANLP Conference on Recent Advances in Natural Language Processing; Borovetz, Bulgaria. 2003. p. 212–9.
  • Miller G, Beckwith R, Fellbaum C, Gross D, Miller K. Five papers on WordNet. CSL Report 43: Technical Report. USA: Cognitive Science Laboratory, Princeton University; 1990.
  • Halliday M, Hasan R. Cohesion in English. London: Longman; 1976.
  • Budanitsky A, Hirst G. Evaluating WordNet-based measures of semantic distance. Computational Linguistics. 2006; 32(1):13–47.
  • Green BF, Wolf AK, Chomsky C, Laughery K. Baseball: An automatic question-answerer. IRE-AIEE-ACM ’61 Western Papers presented at the Western Joint IRE-AIEE-ACM Computer Conference; 1961. p. 219–24.
  • Gildea D, Jurafsky D. Automatic labelling of semantic roles. Computational Linguistics. 2002; 28(3):245–88.
  • Miller A, Walter Charles G. Contextual correlates of semantic similarity. Language and Cognitive Processes. 1991; 6(1):1–28.
  • Bollegala D, Matsuo Y, Ishizuka M. A web search enginebased approach to measure semantic similarity between words. IEEE Transactions on Knowledge and Data Engineering. 2011; 23(7):977–90.
  • Pizzato LAS, Molla-Aliod D. Extracting exact answers using a Meta question Answering system. Proceedings of the Australasian Language Technology Workshop. ALTW05; 2005. p. 1–8.
  • Ferrandez O, Izquierdo R, Ferrandez S, Vicedo J. Addressing ontology-based question answering with collections of user queries. Information Processing and Management. 2009: 45(2):175–88.
  • Mohan K, Aramudhan M. Ontology based access control model for healthcare system in cloud computing. Indian Journal of Science and Technology. 2015 May; 8(S9):213–7. DOI: 10.17485/ijst/2015/v8iS9/53617.
  • Gayathri R, Rajalakshmi V. Connecting health seekers and health care knowledge portal by a semantic approach. Indian Journal of Science and Technology. 2016 Mar; 9(10):1–7. DOI: 10.17485/ijst/2016/v9i10/88889.
  • Rubenstein H,Goodenough JB. Contextual correlates of synonymy. Communications of the ACM. 1965 Oct; 8(10):627–33.
  • Gabrilovich E, Markovitch S. Wikipedia-based semantic interpretation for Natural Language Processing. Journal of Artificial Intelligence Research. 2009; 34:443–98.

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