Abstract
In this paper, we study query expansion strategies that improve the relevance of retrieved documents in a news and social media monitoring system, which performs real-time searches based on complex queries. We propose a two-step retrieval strategy using textual features such as bi-gram word dependencies, proximity, and expansion terms. We compare two different methods for query expansion: (1) based on word co-occurrence information; (2) using semantically-related expansion terms. We evaluate our methods and compare them with the baseline version of the system by crowdsourcing user-centric tasks. The results show that word co-occurrence outperforms semantic query expansion, and improves over the baseline in terms of relevance and utility.
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Arguello, J., Diaz, F., Callan, J., Crespo, J.: Sources of evidence for vertical selection. In: Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 315–322 (2009)
Cao, G., Nie, J., Gao, J., Robertson, S.: Selecting good expansion terms for pseudo-relevance feedback. In: Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 243–250 (2008)
Cilibrasi, R., Vitanyi, P.M.: The Google similarity distance. IEEE Trans. Knowl. Data Eng. 19, 370–383 (2007)
Habibi, M., Popescu-Belis, A.: Using crowdsourcing to compare document recommendation strategies for conversations. In: Workshop on Recommendation Utility Evaluation, Held in Conjunction with ACM RecSys (2012)
Manning, C., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
Ponte, J.M., Croft, B.: A language modeling approach to information retrieval.In: Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 275–281 (1998)
Tablan, V., Bontcheva, K., Roberts, I.: Mímir: an open-source semantic search framework for interactive information seeking and discovery. Web Semant. Sci. Serv. Agents World Wide Web 30, 52–68 (2015)
Zhao, L., Callan, J.: Term necessity prediction. In: Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM), pp. 259–268 (2010)
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This work was funded by the Swiss Commission for Technology and Innovation.
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© 2016 Springer International Publishing Switzerland
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Mahdabi, P., Popescu-Belis, A. (2016). Comparing Two Strategies for Query Expansion in a News Monitoring System. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2016. Lecture Notes in Computer Science(), vol 9612. Springer, Cham. https://doi.org/10.1007/978-3-319-41754-7_24
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DOI: https://doi.org/10.1007/978-3-319-41754-7_24
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