International Journal of System & Software Engineering

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Improving the Performance of RDQA Using Lexical Based Inference Extraction


Affiliations
1 Computer Science and Engineering Department, Anand institute of Higher Technology, Chennai, Tamil Nadu, India
     

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This paper presents an enhanced approach for Question Classification and Answer Extraction in Restricted Domain Question Answering (RDQA). Question Classification and Answer Extraction is the core problem of RDQA and determines the performance of the Question Answering in the Restricted Domain. The proposed approach improves the performance of RDQA by means of (1) Question type prediction model based on Bayesian classification (2) Lexicalized-Index based Passage Retrieval (3) Lexical-Semantic based Inference Extraction. This paper also describes usercentered task-based evaluations for Answer Validation. Further improvements are achieved by combining our model with the classic one to improve the performance of Restricted Domain Question Answering.

Keywords

Restricted Domain Question Answering (RDQA), Bayesian Classification, Passage Retrieval, Answer Extraction, Text Inference.
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  • Benamara, F. (2004). Cooperative question answering in restricted domain:the WEBCOOP experiment, In Proceedings ACL 2004 Workshop on Question Answering in Restricted Domains.

  • Chung, H., Song, Y., Han, K., Yoon, D., Lee, J., Rim, H. & Kim, S.(2004). A Practical QA System in Restricted Domains, In Proceedings ACL 2004Workshop onQuestion Answering in Restricted Domain.

  • Gabrilovich, E. & Markovitch, S. (2007). Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis. In Proceedings of the 20th International Joint Conference on Artificial Intelligence.(IJCAI'07), (pp. 1606-1611).

  • Strube, M. & Ponzetto, S.P.(2006). Wiki Relate! Computing Semantic Relatedness using Wikipedia. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (pp. 1419-1424).

  • Nguyen,H.D. & Kosseim,L. (2004). Using Semantic Information to Improve the Performance of a Restricted-Domain Question-Answering System, In Proceedings ofthe Question-Answering workshop of TALN (TraitementAutomatiquedela Langue Naturelle), Fes, Maroc, pp.441-450.

  • Niu, Y. & Hirst, G. (2004). Analysis of Semantic Classes in Medical Text for Question Answering, In ProceedingsACL 2004 Workshop on Question Answering in Restricted Domains.

  • Voorhees, E. M. & Tice, D. M. (2000). Implementing a Question Answering Evaluation. In Proceedings of LREC' 2000 Workshop on Using Evaluation within HLT Programs: Results and Trends. (pp. 130).

  • Liang, X., Wang, D. & Huang, M. (2010). "Improved Sentence Similarity Algorithm based on VSM and its application in Question Answering System," In Proceedings of the Intelligent Computing and Intelligent Systems (ICIS), Japan.

  • Diekema, A. R., Yilmazel, O., Chen, J., Harwell, S., He, L. & Liddy, E. D. Finding Answers to Complex Questions. To appaer. In Maybury, M.(Ed.) New Directions in Question Answering. AAAI-MIT Press.

  • Gaizauskas, R. & Humphreys, K. (1998). A Combined IR/NLP Approach to Question Answering Against Large Text Collections. University of Sheffield UK.

  • Li, X.& Roth, D. (2002). Learning Question Classifiers. In Proceedings of the 19th International Conference on Computational linguistics (pp. 1-7).


Abstract Views: 332

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  • Improving the Performance of RDQA Using Lexical Based Inference Extraction

Abstract Views: 332  |  PDF Views: 2

Authors

Renita Raymond
Computer Science and Engineering Department, Anand institute of Higher Technology, Chennai, Tamil Nadu, India
Karnavel Kuppusamy
Computer Science and Engineering Department, Anand institute of Higher Technology, Chennai, Tamil Nadu, India

Abstract


This paper presents an enhanced approach for Question Classification and Answer Extraction in Restricted Domain Question Answering (RDQA). Question Classification and Answer Extraction is the core problem of RDQA and determines the performance of the Question Answering in the Restricted Domain. The proposed approach improves the performance of RDQA by means of (1) Question type prediction model based on Bayesian classification (2) Lexicalized-Index based Passage Retrieval (3) Lexical-Semantic based Inference Extraction. This paper also describes usercentered task-based evaluations for Answer Validation. Further improvements are achieved by combining our model with the classic one to improve the performance of Restricted Domain Question Answering.

Keywords


Restricted Domain Question Answering (RDQA), Bayesian Classification, Passage Retrieval, Answer Extraction, Text Inference.

References