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Using a Stack Decoder for Structured Search

  • Kien Tjin-Kam-Jet
  • Dolf Trieschnigg
  • Djoerd Hiemstra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8132)

Abstract

We describe a novel and flexible method that translates free-text queries to structured queries for filling out web forms. This can benefit searching in web databases which only allow access to their information through complex web forms. We introduce boosting and discounting heuristics, and use the constraints imposed by a web form to find a solution both efficiently and effectively. Our method is more efficient and shows improved performance over a baseline system.

Keywords

Retrieval Performance Collective Test Keyword Query Partial Path Travel Planning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Baeza-Yates, R., Castillo, C., Junqueira, F., Plachouras, V., Silvestri, F.: Challenges on distributed web retrieval. In: ICDE 2007, pp. 6–20 (April 2007)Google Scholar
  2. 2.
    Bahl, L.R., Jelinek, F., Mercer, R.L.: A maximum likelihood approach to continuous speech recognition. In: Readings in Speech Recognition, pp. 308–319. Morgan Kaufmann Publishers Inc., San Francisco (1990)Google Scholar
  3. 3.
    Borkar, V., Deshmukh, K., Sarawagi, S.: Automatic segmentation of text into structured records. In: SIGMOD 2001, pp. 175–186. ACM, New York (2001)Google Scholar
  4. 4.
    Chang, K.C.-C., He, B., Li, C., Patel, M., Zhang, Z.: Structured databases on the web: observations and implications. SIGMOD Record 33(3), 61–70 (2004)CrossRefGoogle Scholar
  5. 5.
    Demeester, T., Nguyen, D., Trieschnigg, D., Develder, C., Hiemstra, D.: What snippets say about pages in federated web search. In: Hou, Y., Nie, J.-Y., Sun, L., Wang, B., Zhang, P. (eds.) AIRS 2012. LNCS, vol. 7675, pp. 250–261. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Forney Jr., G.D.: The viterbi algorithm. Proc. of the IEEE 61(3), 268–278Google Scholar
  7. 7.
    Hagen, M., Potthast, M., Stein, B., Braeutigam, C.: Query segmentation revisited. In: WWW 2011, pp. 97–106. ACM, New York (2011)Google Scholar
  8. 8.
    Heinz, S., Zobel, J., Williams, H.E.: Burst tries: a fast, efficient data structure for string keys. In: TOIS 2002, vol. 20(2), pp. 192–223 (2002)Google Scholar
  9. 9.
    Hiemstra, D., van Leeuwen, D.A.: Creating an information retrieval test corpus for dutch. In: CLIN 2001, Amsterdam, The Netherlands. Language and Computers - Studies in Practical Linguistics, vol. 45, pp. 133–147. Rodopi (2002)Google Scholar
  10. 10.
    Kiseleva, J., Guo, Q., Agichtein, E., Billsus, D., Chai, W.: Unsupervised query segmentation using click data: preliminary results. In: WWW 2010, pp. 1131–1132. ACM, New York (2010)Google Scholar
  11. 11.
    Kiseleva, J., Agichtein, E., Billsus, D.: Mining query structure from click data: a case study of product queries. In: CIKM 2011, pp. 2217–2220. ACM, New York (2011)Google Scholar
  12. 12.
    Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: ICML 2001, San Francisco, CA, USA, pp. 282–289. Morgan Kaufmann Publishers Inc. (2001)Google Scholar
  13. 13.
    Li, X., Wang, Y.-Y., Acero, A.: Extracting structured information from user queries with semi-supervised conditional random fields. In: SIGIR 2009, pp. 572–579. ACM, New York (2009)Google Scholar
  14. 14.
    Li, Y., Hsu, B.-J.P., Zhai, C., Wang, K.: Unsupervised query segmentation using clickthrough for information retrieval. In: SIGIR 2011, pp. 285–294. ACM, New York (2011)Google Scholar
  15. 15.
    Madhavan, J., Ko, D., Kot, L., Ganapathy, V., Rasmussen, A., Halevy, A.: Google’s deep web crawl. Proc. VLDB Endow. 1(2), 1241–1252 (2008)Google Scholar
  16. 16.
    Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. of the IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  17. 17.
    Sarkas, N., Paparizos, S., Tsaparas, P.: Structured annotations of web queries. In: SIGMOD 2010, pp. 771–782. ACM, New York (2010)Google Scholar
  18. 18.
    Voorhees, E.M.: Variations in relevance judgments and the measurement of retrieval effectiveness. Inf. Processing and Management 36(5), 697–716 (2000)CrossRefGoogle Scholar
  19. 19.
    Yu, X., Shi, H.: Query segmentation using conditional random fields. In: KEYS 2009, pp. 21–26. ACM, New York (2009)Google Scholar
  20. 20.
    Zhang, Y., Clark, S.: Syntactic processing using the generalized perceptron and beam search. Computational Linguistics 37(1), 105–151 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kien Tjin-Kam-Jet
    • 1
  • Dolf Trieschnigg
    • 1
  • Djoerd Hiemstra
    • 1
  1. 1.University of TwenteEnschedeThe Netherlands

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