A Trie-structured Bayesian Model for Unsupervised Morphological Segmentation

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2017-04-23

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Kurfalı, Murathan
Ustun, Ahmet
CAN BUĞLALILAR, BURCU

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In this paper, we introduce a trie-structured Bayesian model for unsupervised morphological segmentation. We adopt prior information from different sources in the model. We use neural word embeddings to discover words that are morphologically derived from each other and thereby that are semantically similar. We use letter successor variety counts obtained from tries that are built by neural word embeddings. Our results show that using different information sources such as neural word embeddings and letter successor variety as prior information improves morphological segmentation in a Bayesian model. Our model outperforms other unsupervised morphological segmentation models on Turkish and gives promising results on English and German for scarce resources.

Subject Keywords

Bayesian learning, Morphological segmentation, Morphology, Unsupervised learning

URI

https://hdl.handle.net/11511/56841

DOI

https://doi.org/10.1007/978-3-319-77113-7_7

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Graduate School of Informatics, Conference / Seminar

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Citation Formats

M. Kurfalı, A. Ustun, and B. CAN BUĞLALILAR, “A Trie-structured Bayesian Model for Unsupervised Morphological Segmentation,” 2017, vol. 10761, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56841.