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Incorporating word embeddings in unsupervised morphological segmentation

Published online by Cambridge University Press:  10 July 2020

Ahmet Üstün  and
Burcu Can Open the ORCID record for Burcu Can [Opens in a new window]
Ahmet Üstün
Affiliation:
The University of Groningen, Groningen, The Netherlands
Burcu Can*
Affiliation:
Department of Computer Engineering, Hacettepe University, Ankara, Turkey
*
*Corresponding author. E-mail: [email protected]
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Abstract

We investigate the usage of semantic information for morphological segmentation since words that are derived from each other will remain semantically related. We use mathematical models such as maximum likelihood estimate (MLE) and maximum a posteriori estimate (MAP) by incorporating semantic information obtained from dense word vector representations. Our approach does not require any annotated data which make it fully unsupervised and require only a small amount of raw data together with pretrained word embeddings for training purposes. The results show that using dense vector representations helps in morphological segmentation especially for low-resource languages. We present results for Turkish, English, and German. Our semantic MLE model outperforms other unsupervised models for Turkish language. Our proposed models could be also used for any other low-resource language with concatenative morphology.

Keywords

Morphological segmentationUnsupervised learningBayesian learningLow-resource language
Type
Article
Information
Natural Language Engineering , Volume 27 , Issue 5 , September 2021 , pp. 609 - 629
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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