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A Hybrid Approach to Answer Selection in Question Answering Systems

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10758))

Abstract

In this paper, we present a hybrid model for answer selection in question answering systems by representing multiple kinds of features, i.e., lexical-based, word-alignment, and word-embedding. The model employs convolutional neural network, multilayer perceptron, and support vector machines to train the classifiers. We evaluate our model on the two popular QA datasets, SemEval-2016 Task 3 and TREC QA. The experimental results show that our system outperforms the top-5 proposed systems in SemEval-2016 workshop, and also achieves the-state-of-art results on TREC QA dataset.

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Notes

  1. 1.

    http://alt.qcri.org/semeval2016/task3/.

  2. 2.

    http://trec.nist.gov/data/qa.html.

  3. 3.

    http://wordnet.princeton.edu/.

  4. 4.

    https://github.com/snover/terp.

  5. 5.

    http://www.cs.cmu.edu/~alavie/METEOR/.

  6. 6.

    https://code.google.com/archive/p/word2vec/.

References

  1. Ferrucci, D., Brown, E., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A.A., Lally, A., Murdock, J.W., Nyberg, E., Prager, J., Schlaefer, N., Welty, C.: Building Watson: An overview of the DeepQA project. AI Mag. 31(3), 59–79 (2010)

    Article  Google Scholar 

  2. Jijkoun, V., Rijke, M.: Recognizing textual entailment using lexical similarity. In: Proceedings Pascal 2005 Textual Entailment Challenge Workshop (2005)

    Google Scholar 

  3. Duong, P.H., Nguyen, H.T., Nguyen, V.P.: Evaluating semantic relatedness between concepts. In: IMCOM, pp. 20:1–20:8. ACM (2016)

    Google Scholar 

  4. Mollá, D.: Towards semantic-based overlap measures for question answering. In: Proceedings of the First Australasian Language Technology Workshop (ALTW 2003). University of Melbourne, Melbourne (2003)

    Google Scholar 

  5. Harabagiu, S., Moldovan, D., Pasca, M., Surdeanu, M., Mihalcea, R., Girju, R., Rus, V., Lactusu, F., Morarescu, P., Bunescu, R.: Answering complex, list and context questions with LCC’s question-answering server. In: Text REtrieval Conference (TREC) TREC 2001 Proceedings. Department of Commerce, National Institute of Standards and Technology, pp. 355–362 (2001)

    Google Scholar 

  6. Pasupat, P., Liang, P.: Inferring logical forms from denotations. In: ACL (1). The Association for Computer Linguistics (2016)

    Google Scholar 

  7. Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: ACL, pp. 311–318. ACL (2002)

    Google Scholar 

  8. Franco-Salvador, M., Kar, S., Solorio, T., Rosso, P.: UH-PRHLT at SemEval-2016 task 3: combining lexical and semantic-based features for community question answering. In: Bethard, S., Cer, D.M., Carpuat, M., Jurgens, D., Nakov, P., Zesch, T. (eds.) Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2016, San Diego, CA, USA, 16–17 June 2016, pp. 814–821. The Association for Computer Linguistics (2016)

    Google Scholar 

  9. Wang, M., Manning, C.D.: Probabilistic tree-edit models with structured latent variables for textual entailment and question answering. In: Huang, C.R., Jurafsky, D. (eds.) COLING, pp. 1164–1172. Tsinghua University Press (2010)

    Google Scholar 

  10. Duong, P.H., Nguyen, H.T., Huynh, N.-T.: Measuring similarity for short texts on social media. In: Nguyen, H.T.T., Snasel, V. (eds.) CSoNet 2016. LNCS, vol. 9795, pp. 249–259. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42345-6_22

    Chapter  Google Scholar 

  11. Madnani, N., Tetreault, J.R., Chodorow, M.: Re-examining machine translation metrics for paraphrase identification. In: HLT-NAACL, pp. 182–190. The Association for Computational Linguistics (2012)

    Google Scholar 

  12. Yao, X., Durme, B.V., Callison-Burch, C., Clark, P.: Answer extraction as sequence tagging with tree edit distance. In: Vanderwende, L., Daumé III, H., Kirchhoff, K. (eds.) HLT-NAACL, pp. 858–867. The Association for Computational Linguistics (2013)

    Google Scholar 

  13. McCaffery, M., Nederhof, M.J.: DTED: evaluation of machine translation structure using dependency parsing and tree edit distance. In: WMT, pp. 491–498. The Association for Computer Linguistics (2016)

    Google Scholar 

  14. Li, X., Roth, D.: Learning question classifiers. In: Proceedings of the 19th International Conference on Computational Linguistics, vol. 1. Association for Computational Linguistics (2002)

    Google Scholar 

  15. Zhang, D., Lee, W.S.: Question classification using support vector machines. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 26–32. ACM (2003)

    Google Scholar 

  16. Yu, L., Hermann, K.M., Blunsom, P., Pulman, S.: Deep learning for answer sentence selection. In: NIPS Deep Learning Workshop (2014)

    Google Scholar 

  17. Bonadiman, D., Uva, A.E., Moschitti, A.: Multitask learning with deep neural networks for community question answering. CoRR abs/1702.03706 (2017)

    Google Scholar 

  18. He, H., Gimpel, K., Lin, J.J.: Multi-perspective sentence similarity modeling with convolutional neural networks. In: Màrquez, L., Callison-Burch, C., Su, J., Pighin, D., Marton, Y. (eds.) Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, 17–21 September 2015, pp. 1576–1586. The Association for Computational Linguistics (2015)

    Google Scholar 

  19. Huang, J., Yao, S., Lyu, C., Ji, D.: Multi-granularity neural sentence model for measuring short text similarity. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10177, pp. 439–455. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55753-3_28

    Chapter  Google Scholar 

  20. Pagliardini, M., Gupta, P., Jaggi, M.: Unsupervised learning of sentence embeddings using compositional n-gram features. CoRR abs/1703.02507 (2017)

    Google Scholar 

  21. Zhang, X., Li, S., Sha, L., Wang, H.: Attentive interactive neural networks for answer selection in community question answering. In: Singh, S.P., Markovitch, S. (eds.) Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, California, USA, pp. 3525–3531. AAAI Press (2017)

    Google Scholar 

  22. Snover, M.G., Madnani, N., Dorr, B.J., Schwartz, R.M.: Ter-plus: paraphrase, semantic, and alignment enhancements to translation edit rate. Mach. Transl. 23(2–3), 117–127 (2009)

    Article  Google Scholar 

  23. Denkowski, M., Lavie, A.: Meteor 1.3: automatic metric for reliable optimization and evaluation of machine translation systems. In: Proceedings of the Sixth Workshop on Statistical Machine Translation, pp. 85–91. Association for Computational Linguistics (2011)

    Google Scholar 

  24. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013)

    Google Scholar 

  25. Wang, D., Nyberg, E.: A long short-term memory model for answer sentence selection in question answering. In: ACL (2), pp. 707–712. The Association for Computer Linguistics (2015)

    Google Scholar 

  26. Kim, Y.: Convolutional neural networks for sentence classification. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, Doha, Qatar. A Meeting of SIGDAT a Special Interest Group of the ACL, 25–29 October 2014, pp. 1746–1751. ACL (2014)

    Google Scholar 

  27. Nakov, P., Màrquez, L., Moschitti, A., Magdy, W., Mubarak, H., Freihat, A.A., Glass, J., Randeree, B.: SemEval-2016 task 3: community question answering. In: Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2016, San Diego, CA, USA, 16–17 June 2016, pp. 525–545. The Association for Computer Linguistics (2016)

    Google Scholar 

  28. dos Santos, C.N., Tan, M., Xiang, B., Zhou, B.: Attentive pooling networks. CoRR abs/1602.03609 (2016)

    Google Scholar 

  29. Rao, J., He, H., Lin, J.J.: Noise-contrastive estimation for answer selection with deep neural networks. In Mukhopadhyay, S., Zhai, C., Bertino, E., Crestani, F., Mostafa, J., Tang, J., Si, L., Zhou, X., Chang, Y., Li, Y., Sondhi, P. (eds.) Proceedings of the 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA, 24–28 October 2016, pp. 1913–1916. ACM (2016)

    Google Scholar 

  30. Wang, Z., Hamza, W., Florian, R.: Bilateral multi-perspective matching for natural language sentences. CoRR abs/1702.03814 (2017)

    Google Scholar 

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Correspondence to Hien T. Nguyen .

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Duong, P.H., Nguyen, H.T., Nguyen, D.D., Do, H.T. (2018). A Hybrid Approach to Answer Selection in Question Answering Systems. In: Huynh, VN., Inuiguchi, M., Tran, D., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2018. Lecture Notes in Computer Science(), vol 10758. Springer, Cham. https://doi.org/10.1007/978-3-319-75429-1_16

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  • DOI: https://doi.org/10.1007/978-3-319-75429-1_16

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