Abstract
In this paper, we propose an approach to search for the best semantic match of a user query for the question answering system. To achieve this, we make use of word embeddings with a help of trained model using the question answering corpus and its variations to detect the word senses of search queries by the user and show the top best matches which belongs to the same class of question answering pairs and retrieves the corresponding answer to the user. This solution is deployed in ticketing system in large IT industry to automate the user query to retrieve the answers. Word level to context level semantics are achieved through trained model of semantic knowledge with word embeddings.
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References
Li, H., Xu, J.: Semantic matching in search. Found. Trends Inf. Retr. 7, 343–469 (2013)
Berger, A., Caruana, R., Cohn, D., Freitag, D., Mittal, V.: Bridging the lexical chasm: statistical approaches to answer-finding. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 192–199 (2000)
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3(Feb), 1137–1155 (2003)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems (NIPS), pp. 3111–3119 (2013)
Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1532–1543 (2014)
Shen, Y., Rong, W., Jiang, N., Peng, B., Tang, J., Xiong, Z.: Word embedding based correlation model for question/answer matching. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-2017), pp. 3511–3517 (2017)
Kutuzov, A., Kuzmenko, E.: Neural embedding language models in semantic clustering of web search results. In: Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016), pp. 3044–3048 (2016)
Wan, S., Lan, Y., Guo, J., Xu, J, Pang, L., Cheng, X.: A deep architecture for semantic matching with multiple positional sentence representations. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 2835–2841 (2016)
Grbovic, M., Djuric, N., Feng, A., Ordentlich, E.: Scalable semantic matching of queries to ads in sponsored search advertising. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016, pp. 375–384 (2016)
Guo, J., Fan, Y., Ai, Q., Croft, W.B.: Semantic matching by non-linear word transportation for information retrieval. In: Proceeding CIKM 2016 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 701–710 (2016)
Molino, P., Aiello, L.M.: Distributed representations for semantic matching in non-factoid question answering. In: Proceedings of Workshop on Semantic Matching in Information Retrieval Co-located with the 37th International {ACM} {SIGIR} Conference on Research and Development in Information Retrieval, SMIR@SIGIR 2014, pp. 38–45 (2014)
Giordani, A., Moschitti, A.: Semantic mapping between natural language questions and SQL queries via syntactic pairing. In: Horacek, H., Métais, E., Muñoz, R., Wolska, M. (eds.) NLDB 2009. LNCS, vol. 5723, pp. 207–221. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12550-8_17
Levy, O., Goldberg, Y.: Dependency-based word embeddings. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Baltimore, Maryland, pp. 302–308 (2014)
Models and Languages: spaCy Usage Documentation. https://spacy.io/. Accessed 08 Nov 2017
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
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Trivedi, S., Agnihotram, G., Jagan, B., Naik, P. (2018). A Question Answering Model Based on Semantic Matcher for Support Ticketing System. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-1813-9_17
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DOI: https://doi.org/10.1007/978-981-13-1813-9_17
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