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Computational Methods for Name Normalization Using Hypocoristic Personal Name Variants

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Multi-source, Multilingual Information Extraction and Summarization

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Abstract

A growing body of research addresses name normalization as part of coreference and entity resolution systems, but the problem of hypocoristics has not been systematically addressed as a component to such systems. In many languages, these name variants are governed by morphological and morphophonological constraints, providing a dataset rich in features that may be used to train and run matching systems. This paper gives a full treatment to the phenomenon of hypocoristics and presents a supervised learning method that takes advantage of their properties to untangle the relationships between hypocoristic name variants and corresponding full form names.

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Notes

  1. 1.

    An earlier version of this work appeared as Driscoll and Yarowsky 2007 [10].

  2. 2.

    Alternative noncontextual learned edit distance measures such as [31] would suffer from problems similar to those seen in traditional Levenshtein distance. Instead we look to the morphological analyzer presented in Sect. 4.4.2 as a linguistically motivated modeling tool.

  3. 3.

    Data was collected from the following websites:http://forum.wordreference.com/showthread.php?t=247679.http://foro.enfemenino.com/forum/prenoms/_f4180_prenoms-Nombres-y-sus-di minutivos.html.http://en.wiktionary.org/wiki/Appendix:Spanish_diminutives_of_given_names.http://www.transparent.com/spanish/diminutivo-de-los-nombres-propios/.

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Authors and Affiliations

  1. Columbia University, New York, NY, USA

    Patricia Driscoll

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  1. Patricia Driscoll
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Correspondence to Patricia Driscoll .

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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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Driscoll, P. (2013). Computational Methods for Name Normalization Using Hypocoristic Personal Name Variants. 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_4

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  • DOI: https://doi.org/10.1007/978-3-642-28569-1_4

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