Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We assign 0.25 as the threshold following [21].
- 2.
In the experiments, we assign \(\lambda =4\).
References
Bordag, S.: Unsupervised knowledge-free morpheme boundary detection. In: Proceedings of the RANLP 2005 (2005)
Bordag, S.: Two-step approach to unsupervised morpheme segmentation. In: Proceedings of 2nd Pascal Challenges Workshop, pp. 25–29 (2006)
Bordag, S.: Unsupervised and knowledge-free morpheme segmentation and analysis. In: Peters, C., et al. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 881–891. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85760-0_113
Can, B.: Statistical models for unsupervised learning of morphology and POS tagging. Ph.D. thesis, Department of Computer Science, The University of York (2011)
Can, B., Manandhar, S.: Probabilistic hierarchical clustering of morphological paradigms. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2012, pp. 654–663. Association for Computational Linguistics (2012)
Casella, G., George, E.I.: Explaining the Gibbs sampler. Am. Stat. 46(3), 167–174 (1992)
Creutz, M.: Unsupervised segmentation of words using prior distributions of morph length and frequency. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, pp. 280–287. Association for Computational Linguistics (2003)
Creutz, M., Lagus, K.: Unsupervised discovery of morphemes. In: Proceedings of the ACL-02 Workshop on Morphological and Phonological Learning, pp. 21–30. Association for Computational Linguistics (2002)
Creutz, M., Lagus, K.: Inducing the morphological lexicon of a natural language from unannotated text. In: Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR 2005), pp. 106–113 (2005)
Creutz, M., Lagus, K.: Unsupervised models for morpheme segmentation and morphology learning. ACM Trans. Speech Lang. Process. 4, 1–34 (2007)
Déjean, H.: Morphemes as necessary concept for structures discovery from untagged corpora. In: Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning, pp. 295–298. Association for Computational Linguistics (1998)
Goldwater, S., Johnson, M., Griffiths, T.L.: Interpolating between types and tokens by estimating power-law generators. In: Weiss, Y., Schölkopf, B., Platt, J. (eds.) Advances in Neural Information Processing Systems, vol. 18, pp. 459–466. MIT Press, Cambridge (2006)
Hafer, M.A., Weiss, S.F.: Word segmentation by letter successor varieties. Inf. Storage Retriev. 10(11–12), 371–385 (1974)
Hankamer, J.: Finite state morphology and left to right phonology. Proc. West Coast Conf. Formal Linguist. 5, 41–52 (1986)
Harris, Z.S.: From phoneme to morpheme. Language 31(2), 190–222 (1955)
Kurimo, M., Lagus, K., Virpioja, S., Turunen, V.: Morpho challenge 2010. http://research.ics.tkk.fi/events/morphochallenge2010/ (2011). Accessed 31 Jan 2017
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013). http://arxiv.org/abs/1301.3781
Narasimhan, K., Barzilay, R., Jaakkola, T.S.: An unsupervised method for uncovering morphological chains. Trans. Assoc. Comput. Linguist. 3, 157–167 (2015)
Snyder, B., Barzilay, R.: Unsupervised multilingual learning for morphological segmentation. In: Proceedings of ACL-08: HLT, pp. 737–745. Association for Computational Linguistics, June 2008
Soricut, R., Och, F.: Unsupervised morphology induction using word embeddings. In: Proceedings of the Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL, pp. 1627–1637. Association for Computational Linguistics (2015)
Üstün, A., Can, B.: Unsupervised morphological segmentation using neural word embeddings. In: Král, P., Martín-Vide, C. (eds.) SLSP 2016. LNCS (LNAI), vol. 9918, pp. 43–53. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45925-7_4
Acknowledgments
This research is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) with the project number EEEAG-115E464.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Kurfalı, M., Üstün, A., Can, B. (2018). A Trie-structured Bayesian Model for Unsupervised Morphological Segmentation. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-77113-7_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77112-0
Online ISBN: 978-3-319-77113-7
eBook Packages: Computer ScienceComputer Science (R0)