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Entity Linking: Finding Extracted Entities in a Knowledge Base

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Multi-source, Multilingual Information Extraction and Summarization

Abstract

In the menagerie of tasks for information extraction, entity linking is a new beast that has drawn a lot of attention from NLP practitioners and researchers recently. Entity Linking, also referred to as record linkage or entity resolution, involves aligning a textual mention of a named-entity to an appropriate entry in a knowledge base, which may or may not contain the entity. This has manifold applications ranging from linking patient health records to maintaining personal credit files, prevention of identity crimes, and supporting law enforcement. We discuss the key challenges present in this task and we present a high-performing system that links entities using max-margin ranking. We also summarize recent work in this area and describe several open research problems.

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Notes

  1. 1.

    http://www.clsp.jhu.edu/~markus/fstrain

  2. 2.

    www.cs.cornell.edu/people/tj/svm_light/svm_rank.html

  3. 3.

    Bunescu and Pasca [13] report learning tens of thousands of support vectors with their “taxonomy” kernel while a linear kernel represents all support vectors with a single weight vector, enabling faster training and prediction.

  4. 4.

    We used multiple lists, including class-specific lists (i.e., for PER, ORG, and GPE) lists extracted from Freebase [41] and Wikipedia redirects. PER, ORG, and GPE are the commonly used terms for entity types for people, organizations and geo-political regions respectively.

  5. 5.

    Without such a limit, the objective function may diverge for certain parameters of the model; we detect such cases and learn to avoid them during training.

  6. 6.

    Data available from www.dredze.com

  7. 7.

    http://en.wikipedia.org/wiki/Help:Infobox

  8. 8.

    http://research.microsoft.com/en-us/um/people/silviu/WebAssistant/TestData/

  9. 9.

    One of the MSNBC news articles is no longer available so we used 759 total entities.

  10. 10.

    We removed Google, FST and conjunction features which reduced system accuracy but increased performance.

  11. 11.

    vs. 2006 version used in [14] We could not get the 2006 version from the author or the Internet.

  12. 12.

    Since our KB was derived from infoboxes, entities not having an infobox were left out.

  13. 13.

    As this article went to press we became aware of the efforts by Mayfield et al. [49] to construct a cross-language entity linking test collection where the language of the knowledge base is English, but query names are in many languages.

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Authors and Affiliations

  1. Department of Computer Science, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD, 21218, USA

    Delip Rao & Mark Dredze

  2. Human Language Technology Center of Excellence, Johns Hopkins University, Baltimore, MD, USA

    Paul McNamee & Mark Dredze

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  1. Delip Rao
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Correspondence to Delip Rao .

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Editors and Affiliations

  1. Universite Sorbonne Nouvelle, LATTICE-CNRS, Ecole Normale Superieure and, rue d'Ulm 45, Paris, 75005, France

    Thierry Poibeau

  2. , Information & Communication Technologies, Universitat Pompeu Fabra, C/ Tanger 122-140, Barcelona, 08018, Spain

    Horacio Saggion

  3. Institute for Computer Science, Polish Acadmey of Science, ul. Jana Kazimierza 5, Warsaw, 01-248, Poland

    Jakub Piskorski

  4. Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2, Helsinki, 00014, Finland

    Roman Yangarber

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Rao, D., McNamee, P., Dredze, M. (2013). Entity Linking: Finding Extracted Entities in a Knowledge Base. In: Poibeau, T., Saggion, H., Piskorski, J., Yangarber, R. (eds) Multi-source, Multilingual Information Extraction and Summarization. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28569-1_5

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  • DOI: https://doi.org/10.1007/978-3-642-28569-1_5

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