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Categorizing Emails Using Machine Learning with Textual Features Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Name2Vec: Personal Names Embeddings

Authors : Jeremy Foxcroft, Adrian d’Alessandro, Luiza Antonie

Published in: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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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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Footnotes
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This is due to the fact that some of the vectors could have negative values as well.
 
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Metadata
Title
Name2Vec: Personal Names Embeddings
Authors
Jeremy Foxcroft
Adrian d’Alessandro
Luiza Antonie
Copyright Year
2019
Book
Advances in Artificial Intelligence

Print ISBN: 978-3-030-18304-2

Electronic ISBN: 978-3-030-18305-9

Copyright Year: 2019

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

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