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A WordNet Expansion-Based Approach for Question Targets Identification and Classification

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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (CCL 2015, NLP-NABD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9427))

Abstract

Question target identification and classification is a fundamental and essential research for finding suitable target answer type in a question answering system, aiming for improving question answering performance by filtering out irrelevant candidate answers. This paper presents a new automated approach for question target classification based on WordNet expansion. Our approach identifies question target words using dependency relations and answer type rules through the investigation of sample questions. Leveraging semantic relations, e.g., hyponymy, we expanse the question target words as features and apply a widely used classifier LibSVM to achieve question target classification. Our experiment datasets are the standard UIUC 5500 annotated questions and TREC 10 question dataset. The performance presents that our approach can achieve an accuracy of 87.9 % with fine gained classification on UIUC dataset and 86.8 % on TREC 10 dataset, demonstrating its effectiveness.

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Notes

  1. 1.

    http://wordnet.princeton.edu/.

  2. 2.

    Available at http://www.cs.ualberta.ca/~lindek/minipar.htm.

  3. 3.

    http://cogcomp.cs.illinois.edu/Data/QA/QC/train_5500.label.

  4. 4.

    http://cogcomp.cs.illinois.edu/Data/QA/QC/TREC_10.label.

References

  1. Le-Hong, P., Phan, X.-H., Nguyen, T.-D.: Using dependency analysis to improve question classification. In: Nguyen, V.-H., Le, A.-C., Huynh, V.N. (eds.). AISC, vol. 326, pp. 673–686Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  2. Ray, S.K., Shailendra, S., Bhagwati, P.J.: A semantic approach for question classification using WordNet and Wikipedia. Pattern Recogn. Lett. 31(13), 1935–1943 (2010)

    Article  Google Scholar 

  3. Moldovan, D., Paşca, M., Harabagiu, S., Surdeanu, M.: Performance issues and error analysis in an open-domain question answering system. ACM Trans. Inf. Syst. (TOIS) 21(2), 133–154 (2003)

    Article  Google Scholar 

  4. Li, X., Roth, D.: Learning question classifiers. In: Proceedings of COLING (2002)

    Google Scholar 

  5. Voorhees, E.M.: Overview of the TREC 2003 Question Answering Track. TREC (2003)

    Google Scholar 

  6. Miller, G., Christiane, F.: WorldNet: An electronic lexical database. MIT Press, Cambridge (1998)

    Google Scholar 

  7. Huang, Z.H., Thint, M., Qin, Z.C: Question classification using head words and their hypernyms. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (2008)

    Google Scholar 

  8. Bakhtyar, M., Kawtrakul, A., Baber, J., Doudpota, S.M.: Creating multi-level class hierarchy for question classification with NP analysis and WordNet. J. Digit. Inf. Manage. 10(6), 379 (2012)

    Google Scholar 

  9. Martin, A.I., Franz, M., Roukos, S.: IBM’s statistical question answering system-TREC-10. In: Proceedings of the Tenth Text REtrieval Conference (TREC) (2001)

    Google Scholar 

  10. Moschitti, A., Quarteroni, S.: Kernels on linguistic structures for answer extraction. In: Proceedings of ACL (2008)

    Google Scholar 

  11. Quarteroni, S., Moschitti, R., Man, S., Basili, R.: Advanced structural representations for question classification and answer re-ranking. LNCS, pp. 234–245 (2007)

    Google Scholar 

  12. Ray, S.K., Singh, S., Joshi, B.P.: A semantic approach for question classification using WordNet and Wikipedia. Pattern Recogn. Lett. 31(13), 1935–1943 (2010)

    Article  Google Scholar 

  13. Wu, Y.Z., Zhang, R.Q., Hu, X.H., Kashioka, H.: Learning unsupervised SVM classifier for answer selection in web question answering. In: Proceedings of EMNLP-CoNLL, pp. 33–41 (2007)

    Google Scholar 

  14. Sasaki, Y.: Question answering as question-biased term extraction: a new approach toward multilingual. In: Proceedings of ACL, pp. 215–222 (2005)

    Google Scholar 

  15. Ogawa, K., Takeuchi, I.: Safe screening of non-support vectors in pathwise SVM computation. In: Proceedings of International Conference on Machine Learning (ICML) (2013)

    Google Scholar 

  16. Yen, S.J., Wu, Y.C., Yang, J.C., Lee, Y.S., Lee, C.J., Liu, J.J.: A support vector machine-based context-ranking model for question answering. Inf. Sci. 224(2), 77–87 (2013)

    Article  Google Scholar 

  17. Hardy, H., Cheah, Y.N.: Question classification using extreme learning machine on semantic features. J. ICT Res. Appl. 7(1), 36–58 (2013)

    Article  Google Scholar 

  18. Jeong, Y., Myaeng, S.-H.: Using wordnet hypernyms and dependency features for phrasal-level event recognition and type classification. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 267–278. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  19. Gao, J.B., Zhang, B.W., Chen, X.H.: A WordNet-based semantic similarity measurement combining edge-counting and information content theory. Eng. Appl. Artif. Intell. 80–88 (2015)

    Article  Google Scholar 

  20. Barbu, E.: Property type distribution in WordNet, corpora and Wikipedia. Expert Syst. Appl. 42(7), 3501–3507 (2015)

    Article  Google Scholar 

  21. Berwick, R.C.: Principles of principle-based parsing, pp. 1–37. Principle-Based Parsing. Springer, Netherlands (1992)

    Book  Google Scholar 

  22. Hao, T.Y., Xu, F.F., Lei, J.S., Liu, W.Y., Li, Q.: Toward automatic answers in user-interactive question answering systems. Int. J. Softw. Sci. Comput. Intell. 3(4), 52–66 (2011)

    Article  Google Scholar 

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Acknowledgements

This work was supported by National Natural Science Foundation of China (grant No. 61403088 and No.61305094).

Author information

Authors and Affiliations

  1. Key Lab of Language Engineering and Computing of Guangdong Province, Guangdong University of Foreign Studies, Guangzhou, China

    Tianyong Hao

  2. Cisco School of Informatics, Guangdong University of Foreign Studies, Guangzhou, China

    Tianyong Hao & Wenxiu Xie

  3. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, China

    Feifei Xu

Authors
  1. Tianyong Hao
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  2. Wenxiu Xie
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  3. Feifei Xu
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Corresponding author

Correspondence to Tianyong Hao .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Maosong Sun

  2. Tsinghua University, Beijing, China

    Zhiyuan Liu

  3. Soochow University, Suzhou, Jiangsu, China

    Min Zhang

  4. Tsinghua University, Beijing, China

    Yang Liu

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Hao, T., Xie, W., Xu, F. (2015). A WordNet Expansion-Based Approach for Question Targets Identification and Classification. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2015 2015. Lecture Notes in Computer Science(), vol 9427. Springer, Cham. https://doi.org/10.1007/978-3-319-25816-4_27

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  • DOI: https://doi.org/10.1007/978-3-319-25816-4_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25815-7

  • Online ISBN: 978-3-319-25816-4

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