Comparing Two Strategies for Query Expansion in a News Monitoring System

  • Parvaz Mahdabi
  • Andrei Popescu-Belis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9612)


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.


Query analysis Query expansion Web IR and social media search 



This work was funded by the Swiss Commission for Technology and Innovation.


  1. 1.
    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)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    Cilibrasi, R., Vitanyi, P.M.: The Google similarity distance. IEEE Trans. Knowl. Data Eng. 19, 370–383 (2007)CrossRefGoogle Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Manning, C., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)CrossRefzbMATHGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Zhao, L., Callan, J.: Term necessity prediction. In: Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM), pp. 259–268 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Idiap Research InstituteMartignySwitzerland

Personalised recommendations