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Balancing Speed and Accuracy in Neural-Enhanced Phonetic Name Matching

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Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track (ECML PKDD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12979))

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Abstract

Automatic co-text free name matching has a variety of important real-world applications, ranging from fiscal compliance to border control. Name matching systems use a variety of engines to compare two names for similarity, with one of the most critical being phonetic name similarity. In this work, we re-frame existing work on neural sequence-to-sequence transliteration such that it can be applied to name matching. Subsequently, for performance reasons, we then build upon this work to utilize an alternative, non-recurrent neural encoder module. This ultimately yields a model which is 63% faster while still maintaining a 16% improvement in averaged precision over our baseline model.

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Authors and Affiliations

  1. Basis Technology, 1060 Broadway, Somerville, MA, 02144, USA

    Philip Blair, Carmel Eliav, Fiona Hasanaj & Kfir Bar

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  1. Philip Blair
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  2. Carmel Eliav
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  3. Fiona Hasanaj
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Correspondence to Philip Blair .

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Editors and Affiliations

  1. Facebook AI, Seattle, WA, USA

    Yuxiao Dong

  2. Torre Telefonica, Barcelona, Spain

    Nicolas Kourtellis

  3. Bielefeld University, CITEC, Bielefeld, Germany

    Barbara Hammer

  4. Basque Center for Applied Mathematics, Bilbao, Spain

    Jose A. Lozano

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Blair, P., Eliav, C., Hasanaj, F., Bar, K. (2021). Balancing Speed and Accuracy in Neural-Enhanced Phonetic Name Matching. In: Dong, Y., Kourtellis, N., Hammer, B., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12979. Springer, Cham. https://doi.org/10.1007/978-3-030-86517-7_17

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  • DOI: https://doi.org/10.1007/978-3-030-86517-7_17

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