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Computational Methods for Name Normalization Using Hypocoristic Personal Name Variants

  • Patricia Driscoll
Chapter
Part of the Theory and Applications of Natural Language Processing book series (NLP)

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.

Keywords

Machine Translation Full Form Entity Resolution Levenshtein Distance Markedness Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Columbia UniversityNew YorkUSA

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