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Name2Vec: Personal Names Embeddings

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Advances in Artificial Intelligence (Canadian AI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11489))

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Abstract

Predicting if two names refer to the same entity is an important task for many domains, such as information retrieval, record linkage and data integration. In this paper, we propose to create name-embeddings by employing a Doc2Vec methodology, where each name is viewed as a document and each letter in the name is considered a word. Our hypothesis is that representing names as documents, with letters as words, will help capture the internal structure of names and relationships among letters. We present and discuss an experimental study where we explore the effect of various parameters, and we assess the stability of the models built for the embedding of names. Our results show that the new proposed method can predict with high accuracy when a pair of names matches.

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Notes

  1. 1.

    This is due to the fact that some of the vectors could have negative values as well.

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Guelph, Guelph, Canada

    Jeremy Foxcroft, Adrian d’Alessandro & Luiza Antonie

Authors
  1. Jeremy Foxcroft
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  2. Adrian d’Alessandro
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  3. Luiza Antonie
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Corresponding author

Correspondence to Luiza Antonie .

Editor information

Editors and Affiliations

  1. University of Quebec in Montreal, Montreal, QC, Canada

    Marie-Jean Meurs

  2. University of Toronto, Toronto, ON, Canada

    Frank Rudzicz

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Foxcroft, J., d’Alessandro, A., Antonie, L. (2019). Name2Vec: Personal Names Embeddings. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_52

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  • DOI: https://doi.org/10.1007/978-3-030-18305-9_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18304-2

  • Online ISBN: 978-3-030-18305-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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