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International Conference on Statistical Language and Speech Processing

SLSP 2016: Statistical Language and Speech Processing pp 43–53Cite as

Unsupervised Morphological Segmentation Using Neural Word Embeddings

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9918)

Abstract

We present a fully unsupervised method for morphological segmentation. Unlike many morphological segmentation systems, our method is based on semantic features rather than orthographic features. In order to capture word meanings, word embeddings are obtained from a two-level neural network [11]. We compute the semantic similarity between words using the neural word embeddings, which forms our baseline segmentation model. We model morphotactics with a bigram language model based on maximum likelihood estimates by using the initial segmentations from the baseline. Results show that using semantic features helps to improve morphological segmentation especially in agglutinating languages like Turkish. Our method shows competitive performance compared to other unsupervised morphological segmentation systems.

Keywords

  • Morphology
  • Semantics
  • Neural representation of speech and language
  • Morphological segmentation
  • Unsupervised learning
  • Word embeddings

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Acknowledgements

This research was supported by TUBITAK (The Scientific and Technological Research Council of Turkey) grant number 115E464.

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Authors and Affiliations

  1. Cognitive Science Department, Informatics Institute, Middle East Technical University (ODTÜ), Ankara, 06800, Turkey

    Ahmet Üstün

  2. Department of Computer Engineering, Hacettepe University, Beytepe, Ankara, 06800, Turkey

    Burcu Can

Authors
  1. Ahmet Üstün
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  2. Burcu Can
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Corresponding authors

Correspondence to Ahmet Üstün or Burcu Can .

Editor information

Editors and Affiliations

  1. University of West Bohemia , Plzen, Czech Republic

    Pavel Král

  2. Rovira i Virgili University , Tarragona, Spain

    Carlos Martín-Vide

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Üstün, A., Can, B. (2016). Unsupervised Morphological Segmentation Using Neural Word Embeddings. In: Král, P., Martín-Vide, C. (eds) Statistical Language and Speech Processing. SLSP 2016. Lecture Notes in Computer Science(), vol 9918. Springer, Cham. https://doi.org/10.1007/978-3-319-45925-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-45925-7_4

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