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2022 | OriginalPaper | Chapter

Evaluating the Use of Synthetic Queries for Pre-training a Semantic Query Tagger

Authors : Elias Bassani, Gabriella Pasi

Published in: Advances in Information Retrieval

Publisher: Springer International Publishing

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Abstract

Semantic Query Labeling is the task of locating the constituent parts of a query and assigning domain-specific semantic labels to each of them. It allows unfolding the relations between the query terms and the documents’ structure while leaving unaltered the keyword-based query formulation. In this paper, we investigate the pre-training of a semantic query-tagger with synthetic data generated by leveraging the documents’ structure. By simulating a dynamic environment, we also evaluate the consistency of performance improvements brought by pre-training as real-world training data becomes available. The results of our experiments suggest both the utility of pre-training with synthetic data and its improvements’ consistency over time.

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Balog, K.: Entity-Oriented Search, The Information Retrieval Series, vol. 39. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93935-3

Bassani, E., Pasi, G.: On building benchmark datasets for understudied information retrieval tasks: the case of semantic query labeling. In: Anelli, V.W., Noia, T.D., Ferro, N., Narducci, F. (eds.) Proceedings of the 11th Italian Information Retrieval Workshop 2021, Bari, Italy, 13–15 September 2021. CEUR Workshop Proceedings, vol. 2947. CEUR-WS.org (2021). http://ceur-ws.org/Vol-2947/paper16.pdf

Bassani, E., Pasi, G.: Semantic query labeling through synthetic query generation. In: SIGIR 2021: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, 11–15 July 2021, pp. 2278–2282. ACM (2021). https://doi.org/10.1145/3404835.3463071, https://doi.org/10.1145/3404835.3463071

Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2–7 June 2019, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/n19-1423, https://doi.org/10.18653/v1/n19-1423

Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

Kozareva, Z., Li, Q., Zhai, K., Guo, W.: Recognizing salient entities in shopping queries. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, 7–12 August 2016, Berlin, Germany, Volume 2: Short Papers. The Association for Computer Linguistics (2016). https://doi.org/10.18653/v1/p16-2018, https://doi.org/10.18653/v1/p16-2018

Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Brodley, C.E., Danyluk, A.P. (eds.) Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28–July 1 2001, pp. 282–289. Morgan Kaufmann (2001)

Li, X.: Understanding the semantic structure of noun phrase queries. In: Hajic, J., Carberry, S., Clark, S. (eds.) ACL 2010, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 11–16 July 2010, Uppsala, Sweden, pp. 1337–1345. The Association for Computer Linguistics (2010). https://www.aclweb.org/anthology/P10-1136/

10.

Li, X., Wang, Y., Acero, A.: Extracting structured information from user queries with semi-supervised conditional random fields. In: Allan, J., Aslam, J.A., Sanderson, M., Zhai, C., Zobel, J. (eds.) Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, Boston, MA, USA, 19–23 July 2009, pp. 572–579. ACM (2009). https://doi.org/10.1145/1571941.1572039, https://doi.org/10.1145/1571941.1572039

11.

Liu, J., Li, X., Acero, A., Wang, Y.: Lexicon modeling for query understanding. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, 22–27 May 2011, Prague Congress Center, Prague, Czech Republic, pp. 5604–5607. IEEE (2011). https://doi.org/10.1109/ICASSP.2011.5947630, https://doi.org/10.1109/ICASSP.2011.5947630

12.

Manshadi, M., Li, X.: Semantic tagging of web search queries. In: Su, K., Su, J., Wiebe, J. (eds.) ACL 2009, Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 2–7 August 2009, Singapore, pp. 861–869. The Association for Computer Linguistics (2009). https://www.aclweb.org/anthology/P09-1097/

13.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C., Bottou, L., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5–8 2013, Lake Tahoe, Nevada, United States, pp. 3111–3119 (2013). https://proceedings.neurips.cc/paper/2013/hash/9aa42b31882ec039965f3c4923ce901b-Abstract.html

14.

Pass, G., Chowdhury, A., Torgeson, C.: A picture of search. In: Jia, X. (ed.) Proceedings of the 1st International Conference on Scalable Information Systems, Infoscale 2006, Hong Kong, May 30-June 1 2006. ACM International Conference Proceeding Series, vol. 152, p. 1. ACM (2006). https://doi.org/10.1145/1146847.1146848, https://doi.org/10.1145/1146847.1146848

15.

Sarkas, N., Paparizos, S., Tsaparas, P.: Structured annotations of web queries. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, pp. 771–782 (2010)

16.

Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: DropOut: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014). http://dl.acm.org/citation.cfm?id=2670313

Title: Evaluating the Use of Synthetic Queries for Pre-training a Semantic Query Tagger
Authors: Elias Bassani
Gabriella Pasi
Publisher: Springer International Publishing
Book: Advances in Information Retrieval
Print ISBN: 978-3-030-99738-0

Electronic ISBN: 978-3-030-99739-7

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-030-99739-7_5

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