Abstract
Question Answering is a challenging topic and is gaining growing attention in last years being an interesting interdisciplinary research area and having practical application. In this paper we focus on the answer selection, a step of Question Answers that selects answers to the questions from among the answer candidates based on the result of question analysis. This process can be very challenging, as it often entails identifying correct answers amongst many incorrect ones. In particular, we focus on the ranking of the answers based on Italian language and referring to a dataset that is closed-domain, containing questions about cultural heritage with successive True or false answers. In this paper we demonstrate that, using an approach based on classification, we can reach a very high accuracy that is better than the accuracy reached with other approaches based on fuzzy logic.
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References
Al-Khoder, A., Harmouch, H.: Evaluating four of the most popular open source and free data mining tools. Int. J. Acad. Sci. Res. 3(1), 13–23 (2015)
Balcan, M.F., Bansal, N., Beygelzimer, A., Coppersmith, D., Langford, J., Sorkin, G.B.: Robust reductions from ranking to classification. Mach. Learn. 72(1), 139–153 (2008). https://doi.org/10.1007/s10994-008-5058-6
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2, 121–167 (1998)
Chu-Carroll, J., Czuba, K., Prager, J., Ittycheriah, A.: In question answering, two heads are better than one. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, NAACL 2003, Vol. 1, pp. 24–31. Association for Computational Linguistics, Stroudsburg, PA, USA (2003). https://doi.org/10.3115/1073445.1073449
Dalip, D.H., Gonçalves, M.A., Cristo, M., Calado, P.: Exploiting user feedback to learn to rank answers in q&a forums: A case study with stack overflow. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013, pp. 543–552. ACM, New York, NY, USA (2013). https://doi.org/10.1145/2484028.2484072
Damiano, E., Spinelli, R., Esposito, M., De Pietro, G.: Towards a framework for closed-domain question answering in Italian. In: Proceedings of 12th International Conference on Signal-Image Technology & Internet-Based Systems, pp. 604–611 (2016)
Gardner, M., Dorling, S.: Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmos. Environ. 32(14–15), 2627–2636 (1998). http://www.sciencedirect.com/science/article/pii/S1352231097004470
Gondek, D.C., Lally, A., Kalyanpur, A., Murdock, J.W., Duboué, P.A., Zhang, L., Pan, Y., Qiu, Z.M., Welty, C.: A framework for merging and ranking of answers in DeepQA. IBM J. Res. Dev. 56(3.4), 14:1–14:12 (2012)
Javanmardi, S., Shojafar, M., Shariatmadari, S., Ahrabi, S.: FR trust: A fuzzy reputation-based model for trust management in semantic P2P grids. Int. J. Grid Util. Comput. 6(1), 57–66 (2015)
Jin, H., Sun, A., Zheng, R., He, R., Zhang, Q.: Ontology-based semantic integration scheme for medical image grid. Int. J. Grid Util. Comput. 1(2), 86–97 (2009)
Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: Proceedings of the 10th European Conference on Machine Learning, ECML 1998, pp. 137–142. Springer, London (1998). http://dl.acm.org/citation.cfm?id=645326.649721
Ko, J., Mitamura, T., Carbonell, J.: Probabilistic approaches for answer selection in multilingual question answering (2007)
Pota, M., Esposito, M., De Pietro, G.: Learning to rank answers to closed-domain questions by using fuzzy logic. In: Proceedings of 12th International Conference on Signal-Image Technology & Internet-Based Systems, pp. 604–611 (2016)
Savenkov, D.: Ranking answers and web passages for non-factoid question answering: Emory university at TREC liveQA. In: TREC (2015)
Verberne, S., van Halteren, H., Theijssen, D., Raaijmakers, S., Boves, L.: Learning to rank for why-question answering. Inf. Retrieval 14(2), 107–132 (2011). https://doi.org/10.1007/s10791-010-9136-6
Wimmer, H., Powell, L.M.: A comparison of open source tools for data science. J. Inf. Syst. Appl. Res. 9(2), 4 (2016)
Acknowledgment
The work has been partly supported by the Italian project PON03PE_00128_1 “eHealthNet: Software ecosystem for Electronic Health”. Authors thank Raffaele Mattiello and Giuseppe Trerotola for their technical support.
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Amato, A., Coronato, A. (2018). A Machine Learning Approach for Ranking in Question Answering. In: Xhafa, F., Caballé, S., Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-69835-9_8
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DOI: https://doi.org/10.1007/978-3-319-69835-9_8
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