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The effect of morphology in named entity recognition with sequence tagging

Published online by Cambridge University Press:  27 July 2018

ONUR GÜNGÖR Open the ORCID record for ONUR GÜNGÖR [Opens in a new window] ,
TUNGA GÜNGÖR  and
SUZAN ÜSKÜDARLI
ONUR GÜNGÖR
Affiliation:
Department of Computer Engineering, Bogazici University, Istanbul Huawei R&D Center, Istanbul, Turkey e-mail: onurgu@boun.edu.tr
TUNGA GÜNGÖR
Affiliation:
Department of Computer Engineering, Bogazici University, Istanbul, Turkey e-mail: gungort@boun.edu.tr, suzan.uskudarli@boun.edu.tr
SUZAN ÜSKÜDARLI
Affiliation:
Department of Computer Engineering, Bogazici University, Istanbul, Turkey e-mail: gungort@boun.edu.tr, suzan.uskudarli@boun.edu.tr
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Abstract

This work proposes a sequential tagger for named entity recognition in morphologically rich languages. Several schemes for representing the morphological analysis of a word in the context of named entity recognition are examined. Word representations are formed by concatenating word and character embeddings with the morphological embeddings based on these schemes. The impact of these representations is measured by training and evaluating a sequential tagger composed of a conditional random field layer on top of a bidirectional long short-term memory layer. Experiments with Turkish, Czech, Hungarian, Finnish and Spanish produce the state-of-the-art results for all these languages, indicating that the representation of morphological information improves performance.

Type
Article
Information
Natural Language Engineering , Volume 25 , Issue 1 , January 2019 , pp. 147 - 169
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

This research was supported by Boğaziçi University Research Fund (BAP) under Grant 13083.

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