Unsupervised Morphological Segmentation Using Neural Word Embeddings

Date

2016-10-12

Author

Ustun, Ahmet
CAN BUĞLALILAR, BURCU

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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.

Subject Keywords

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

URI

https://hdl.handle.net/11511/66056

DOI

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

Collections

Department of Metallurgical and Materials Engineering, Conference / Seminar

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Citation Formats

A. Ustun and B. CAN BUĞLALILAR, “Unsupervised Morphological Segmentation Using Neural Word Embeddings,” 2016, vol. 9918, p. 43, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66056.