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Query Formulation for Action Search by Bootstrapping

  • Yoshinori Kitaguchi
  • Hiroaki Ohshima
  • Katsumi Tanaka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8839)

Abstract

A method to formulate queries to search for concrete, practical, or detailed “actions” on the web is proposed. Sometimes, a user can only express a web search query as an abstract action. For example, if the user is a beginner golfer, they may use “to improve golf” as a web search query. The search results for this query are unlikely to contain many pages about concrete actions related to improving golf skills. To obtain more concrete information, more concrete actions must be used as web queries. The proposed method generates tuples of words such as (shanking, stop) and (distance, adjust), that consist of a noun and a verb. The proposed algorithm repeatedly searches for nouns from verbs and verbs from nouns in a bootstrapping manner. The proposed method verifies the usefulness of tuples. To reduce search costs, the proposed method also excludes useless tuples; i.e., tuples that cannot be used to obtain new useful tuples.

Keywords

bootstrap action search query formulation search-result diversification 

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References

  1. 1.
    Bhagat, R., Hovy, E., Patwardhan, S.: Acquiring paraphrases from text corpora. In: Proc. of K-CAP 2009, pp. 161–168 (2009)Google Scholar
  2. 2.
    Carbonell, J., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: Proc. of SIGIR1998, pp. 335–336 (1998)Google Scholar
  3. 3.
    Chang, C.-H., Lui, S.-C.: Iepad: Information extraction based on pattern discovery. In: Proc. of WWW 2001, pp. 681–688 (2001)Google Scholar
  4. 4.
    Curran, J.R., Murphy, T., Scholz, B.: Minimising semantic drift with Mutual Exclusion Bootstrapping. In: Proc. of PACLING 2007, pp. 172–180 (2007)Google Scholar
  5. 5.
    Kawai, H., Mizuguchi, H., Tsuchida, M.: Cost-effective web search in bootstrapping for named entity recognition. In: Haritsa, J.R., Kotagiri, R., Pudi, V. (eds.) DASFAA 2008. LNCS, vol. 4947, pp. 393–407. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Lin, T., Pantel, P., Gamon, M., Kannan, A., Fuxman, A.: Active objects: Actions for entity-centric search. In: Proc. of WWW 2012, pp. 589–598 (2012)Google Scholar
  7. 7.
    Lucchese, C., Orlando, S., Perego, R., Silvestri, F., Tolomei, G.: Discovering tasks from search engine query logs. ACM Trans. Inf. Syst., pp. 14:1–14:43 (2013)Google Scholar
  8. 8.
    McIntosh, T.: Unsupervised discovery of negative categories in lexicon bootstrapping. In: Proc. of EMNLP 2010, pp. 356–365 (2010)Google Scholar
  9. 9.
    McIntosh, T., Curran, J.R.: Reducing semantic drift with bagging and distributional similarity. In: Proc. of ACL 2009, pp. 396–404 (2009)Google Scholar
  10. 10.
    Pekar, V.: Acquisition of verb entailment from text. In: Proc. of HLT-NAACL 2006, pp. 49–56 (2006)Google Scholar
  11. 11.
    Sang, E.T.K., Hofmann, K.: Lexical patterns or dependency patterns: Which is better for hypernym extraction? In: Proc. of CoNLL 2009, pp. 174–182 (2009)Google Scholar
  12. 12.
    Torisawa, K.: Acquiring inference rules with temporal constraints by using japanese coordinated sentences and noun-verb co-occurrences. In: Proc. of HLT-NAACL 2006, pp. 57–64 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yoshinori Kitaguchi
    • 1
  • Hiroaki Ohshima
    • 1
  • Katsumi Tanaka
    • 1
  1. 1.Department of Social Informatics, Graduate School of InformaticsKyoto UniversitySakyoJapan

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