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Multi-source, Multilingual Information Extraction and Summarization pp 53–71Cite as

Learning to Match Names Across Languages

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Abstract

We report on research on matching names in different scripts across languages. We explore two trainable approaches based on comparing pronunciations. The first, a cross-lingual approach, uses an automatic name-matching program that exploits rules based on phonological comparisons of the two languages carried out by humans. The second, monolingual approach relies only on automatic comparison of the phonological representations of each pair. Alignments produced by each approach are fed to a machine learning algorithm. Results show that the monolingual approach results in machine-learning based comparison of person-names in English and Chinese at an accuracy of over 97.0 F-measure.

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Notes

  1. 1.

    www.informatica.com/solutions/identity_resolution_solution/Pages/index.aspx.

  2. 2.

    www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2005T34.

  3. 3.

    webdocs.cs.ualberta.ca/~kondrak/aline1.1.zip.

  4. 4.

    For the MALINE row in Table 3.3, the ALINE documentation explains the notation as follows: “every phonetic symbol is represented by a single lowercase letter followed by zero or more uppercase letters. The initial lowercase letter is the base letter most similar to the sound represented by the phonetic symbol. The remaining uppercase letters stand for the feature modifiers which alter the sound defined by the base letter. By default, the output contains the alignments together with overall similarity scores. The aligned subsequences are delimited by ‘|’ signs. The ‘<’ sign signifies that the previous phonetic segment has been aligned with two segments in the other sequence, a case of compression/expansion. The ‘–’ sign denotes a “skip”, a case of insertion/deletion.”

  5. 5.

    The Predictive Accuracy was computed with exactly half the test examples being positive.

  6. 6.

    sourceforge.net/projects/carafe.

  7. 7.

    projects.ldc.upenn.edu/LCTL/.

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Acknowledgements

This research has been funded by the MITRE Innovation Program (Public Release Case Number 07–0752). We are also grateful to the reviewers for their insightful comments.

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Authors and Affiliations

  1. The MITRE Corporation, 202 Burlington Road, Bedford, MA, 01730, USA

    Inderjeet Mani & Alex Yeh

  2. The MITRE Corporation, 7515 Colshire Drive, McLean, VA, 22102, USA

    Sherri Condon

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  1. Inderjeet Mani
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Correspondence to Inderjeet Mani .

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  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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Mani, I., Yeh, A., Condon, S. (2013). Learning to Match Names Across Languages. 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_3

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  • DOI: https://doi.org/10.1007/978-3-642-28569-1_3

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