skip to main content
10.1145/2339530.2339743acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

A framework for robust discovery of entity synonyms

Published:12 August 2012Publication History

ABSTRACT

Entity synonyms are critical for many applications like information retrieval and named entity recognition in documents. The current trend is to automatically discover entity synonyms using statistical techniques on web data. Prior techniques suffer from several limitations like click log sparsity and inability to distinguish between entities of different concept classes. In this paper, we propose a general framework for robustly discovering entity synonym with two novel similarity functions that overcome the limitations of prior techniques. We develop efficient and scalable techniques leveraging the MapReduce framework to discover synonyms at large scale. To handle long entity names with extraneous tokens, we propose techniques to effectively map long entity names to short queries in query log. Our experiments on real data from different entity domains demonstrate the superior quality of our synonyms as well as the efficiency of our algorithms. The entity synonyms produced by our system is in production in Bing Shopping and Video search, with experiments showing the significance it brings in improving search experience.

Skip Supplemental Material Section

Supplemental Material

310_w_talk_4.mp4

mp4

364 MB

References

  1. S. Agrawal, K. Chakrabarti, S. Chaudhuri, and V. Ganti. Scalable ad-hoc entity extraction from text collections. Proc. VLDB Endow., 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. M. Baroni and S. Bisi. Using cooccurrence statistics and the web to discover synonyms in technical language. In In Proceedings of LREC 2004, pages 1725--1728, 2004.Google ScholarGoogle Scholar
  3. S. Chaudhuri, V. Ganti, and D. Xin. Exploiting web search to generate synonyms for entities. In WWW Conference, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. Chaudhuri, V. Ganti, and D. Xin. Mining document collections to facilitate accurate approximate entity matching. PVLDB, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. S. Chaudhuri, V. Ganti, and D. Xin. Mining document collections to facilitate accurate approximate entity matching. PVLDB, 2(1), 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. T. Cheng, H. Lauw, and S. Paparizos. Fuzzy matching of web queries to structured data. In ICDE, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  7. T. Cheng, H. W. Lauw, and S. Paparizos. Entity synonyms for structured web search. TKDE, 2011.Google ScholarGoogle Scholar
  8. N. Craswell and M. Szummer. Random walks on the click graph. In SIGIR, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. X. Dong, A. Halevy, and J. Madhavan. Reference reconciliation in complex information spaces. In SIGMOD, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. G. W. Furnas, S. C. Deerwester, S. T. Dumais, T. K. Landauer, R. A. Harshman, L. A. Streeter, and K. E. Lochbaum. Information retrieval using a singular value decomposition model of latent semantic structure. In SIGIR, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Z. Harris. Distributional structure. Word, 10(23), 1954.Google ScholarGoogle Scholar
  12. M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly. Dryad: distributed data-parallel programs from sequential building blocks. In EuroSys, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. R. Jones, B. Rey, O. Madani, and W. Greiner. Generating query substitutions. In WWW, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. C. D. Manning and H. Schütze. Foundations of statistical natural language processing. MIT Press, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Q. Mei, D. Zhou, and K. Church. Query suggestion using hitting time. In CIKM, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. G. Navarro. A guided tour to approximate string matching. ACM Comput. Surv., 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. P. Pantel, E. Crestan, A. Borkovsky, A.-M. Popescu, and V. Vyas. Web-scale distributional similarity and entity set expansion. In EMNLP, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. P. D. Turney. Mining the web for synonyms: Pmi-ir versus lsa on toefl. CoRR, cs.LG/0212033, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. T. Wang and G. Hirst. Near-synonym lexical choice in latent semantic space. In COLING, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. A framework for robust discovery of entity synonyms

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
        August 2012
        1616 pages
        ISBN:9781450314626
        DOI:10.1145/2339530

        Copyright © 2012 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 12 August 2012

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate1,133of8,635submissions,13%

        Upcoming Conference

        KDD '24

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader