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A framework for intelligent question answering system using semantic context-specific document clustering and Wordnet

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Abstract

The question answering system plays an important role in information retrieval field, where the user is in need of getting a precise answer instead of large collections of documents. The aim of this paper is to investigate techniques for improving sentence-based question answering system. To achieve this, a POS-Tagger-based question pattern analysis model is proposed to identify question type based on pattern template for the user-submitted query. Next, the knowledge base is created from a large corpus by clustering the documents by grouping on domain context. The proposed semantic-word-based answer generator model deals with the user query mapping with an appropriate sentence in the knowledge base. By the proposed models, the system reduces the search gap among user queries and answer sentences using Wordnet. It considers word order, overlap, sentence similarity, string distance, unambiguous words and semantic similarity of words. The proposed algorithm evaluates with benchmark datasets such as 20Newsgroup and TREC-9 QA, and proves its efficiency by statistical test for significance.

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References

  1. Lin J 2007 Is question answering better than information retrieval? Towards a task-based evaluation framework for question series. In: Proceedings of Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, pp. 212–219

  2. Yang H, Chua T S, Wang S and Koh C K 2003 Structured use of external knowledge for event-based open domain question answering. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 33–40

  3. Li X and Roth D 2002 Learning question classifiers. In: Proceedings of the 19th International Conference on Computational Linguistics, Association for Computational Linguistics, vol. 1, pp. 1–7

  4. Ahmed W and Babu A P 2016 Question analysis for Arabic question answering systems. Int. J. Natl. Language Computing 5(6): 21–30

    Article  Google Scholar 

  5. Balasubramanian N, Allan J and Croft W B 2007 A comparison of sentence retrieval techniques. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 813–814

  6. Tan M, dos Santos C, Xiang B and Zhou B 2016 Improved representation learning for question answer matching. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 464–473

  7. Moldovan D I and Rus V 2001 Logic form transformation of wordnet and its applicability to question answering. In: Proceedings of the 39th Annual Meeting on Association for Computational Linguistics, pp. 402–409

  8. https://nlp.stanford.edu/. Accessed date Feb 23, 2003

  9. Christos B and Vassilis T 2012 A clustering technique for news articles using WordNet. Knowl. Based Syst. 36: 115–128

  10. Liu S, Liu F, Yu C and Meng W 2004 An effective approach to document retrieval via utilizing WordNet and recognizing phrases. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 266–272

  11. Momtazi S and Klakow D 2015 Bridging the vocabulary gap between questions and answer sentences. Inf. Process. Manag. 51(5): 595–615

    Article  Google Scholar 

  12. Jeon J, Croft W B and Lee J H 2005 Finding semantically similar questions based on their answers. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 617–618

  13. Severyn A, Nicosia M and Moschitti A 2013 Building structures from classifiers for passage reranking. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, ACM, pp. 969–978

  14. http://qwone.com/~jason/20Newsgroups/. Accessed date Jan 14, 2008

  15. http://trec.nist.gov/data/qamain.html. Accessed date Nov 12, 2000

  16. Wu Y, Hori C, Kashioka H and Kawai H 2015 Leveraging social QA collections for improving complex question answering. Comput. Speech Lang. 29(1): pp. 1–19

    Article  Google Scholar 

  17. Smucker M D, Allan J and Carterette B 2007 A comparison of statistical significance tests for information retrieval evaluation. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, ACM, pp. 623–632

  18. Kolomiyets O and Moens M F 2011 A survey on question answering technology from an information retrieval perspective. Inf. Sci. 181(24): pp. 5412–5434

    Article  MathSciNet  Google Scholar 

  19. Pavli M, Han Z D and Jakupovi A 2015 Question answering with a conceptual framework for knowledge-based system development node of knowledge. Expert Syst. Appl. 42(12): pp. 5264–5286

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Applications, Dr Mahalingam College of Engineering and Technology, Pollachi, 642003, India

    K Karpagam

  2. Department of Computer Science and Engineering, Institute of Road and Transport Technology, Erode, India

    A Saradha

Authors
  1. K Karpagam
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  2. A Saradha
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Correspondence to K Karpagam.

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Karpagam, K., Saradha, A. A framework for intelligent question answering system using semantic context-specific document clustering and Wordnet. Sādhanā 44, 62 (2019). https://doi.org/10.1007/s12046-018-1022-8

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  • DOI: https://doi.org/10.1007/s12046-018-1022-8

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