Abstract
We propose a novel Bayesian model for fully unsupervised word segmentation based on monolingual character alignment. Adapted bilingual word alignment models and a Bayesian language model are combined through product of experts to estimate the joint posterior distribution of a monolingual character alignment and the corresponding segmentation. Our approach enhances the performance of conventional hierarchical Pitman-Yor language models with richer character-level features. In the conducted experiments, our model achieves an 88.6% word token f-score on the standard Brent version of the Bernstein-Ratner corpora. Moreover, on standard Chinese segmentation datasets, our method outperforms a baseline model by 1.9-2.9 f-score points.
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References
Wang, H., Zhu, J., Tang, S., Fan, X.: A new unsupervised approach to word segmentation. CL 37, 421–454 (2011)
Sun, M., Shen, D., Tsou, B.K.: Chinese word segmentation without using lexicon and hand-crafted training data. In: Proceedings of the Joint Conference of ACL and COLING, Montreal, Quebec, Canada, pp. 1265–1271. ACL (1998)
Goldwater, S., Griffiths, T.L., Johnson, M.: Contextual dependencies in unsupervised word segmentation. In: Proceedings of the Joint Conference of ACL and COLING, ACL-44, Stroudsburg, PA, USA, pp. 673–680 (2006)
Mochihashi, D., Yamada, T., Ueda, N.: Bayesian unsupervised word segmentation with nested pitman-yor language modeling. In: Proceedings of the Joint Conference of ACL and IJCNLP, ACL 2009, Stroudsburg, PA, USA, pp. 100–108 (2009)
Johnson, M., Goldwater, S.: Improving nonparameteric bayesian inference: experiments on unsupervised word segmentation with adaptor grammars. In: Proceedings of Human Language Technologies: The 2009 NAACL, NAACL 2009, Stroudsburg, PA, USA, pp. 317–325 (2009)
Liu, Z., Wang, H., Wu, H., Li, S.: Collocation extraction using monolingual word alignment method. In: Proceedings of EMNLP, Singapore, pp. 487–495 (2009)
Brody, S.: It depends on the translation: unsupervised dependency parsing via word alignment. In: Proceedings of EMNLP, EMNLP 2010, Stroudsburg, PA, USA, pp. 1214–1222 (2010)
Brown, P.F., Pietra, V.J.D., Pietra, S.A.D., Mercer, R.L.: The mathematics of statistical machine translation: Parameter estimation. Comput. Linguist. 19, 263–311 (1993)
Vogel, S., Ney, H., Tillmann, C.: Hmm-based word alignment in statistical translation. In: Proceedings of COLING, COLING 1996, Stroudsburg, PA, USA, pp. 836–841 (1996)
Teh, Y.W.: A hierarchical bayesian language model based on pitman-yor processes. In: Proceedings of the Joint Conference of ACL and COLING, ACL-44, Stroudsburg, PA, USA, pp. 985–992 (2006)
Bernstein-Ratner, N.: The phonology of parent-child speech. In: Nelson, K., van Kleeck, A. (eds.), vol. 6. Erlbaum, Hillsdale (1987)
Xu, J., Gao, J., Toutanova, K., Ney, H.: Bayesian semi-supervised Chinese word segmentation for statistical machine translation. In: Proceedings of COLING, COLING 2008, Stroudsburg, PA, USA, pp. 1017–1024 (2008)
Nguyen, T., Vogel, S., Smith, N.A.: Nonparametric word segmentation for machine translation. In: Proceedings of COLING, COLING 2010, Stroudsburg, PA, USA, pp. 815–823 (2010)
Chung, T., Gildea, D.: Unsupervised tokenization for machine translation. In: Proceedings of EMNLP, EMNLP 2009, Stroudsburg, PA, USA, pp. 718–726 (2009)
Pitman, J., Yor, M.: The two-parameter Poisson-Dirichlet distribution derived from a stable subordinator (1995)
Goldwater, S., Griffiths, T.L., Johnson, M.: A bayesian framework for word segmentation: Exploring the effects of Context. Cognition 112, 21–54 (2009)
Och, F.J., Ney, H., Josef, F., Ney, O.H.: A systematic comparison of various statistical alignment models. Computational Linguistics 29 (2003)
Tom, E.: Second international Chinese word segmentation bakeoff (2005)
MacWhinney, B., Snow, C., et al.: The child language data exchange system. Journal of Child Language 12, 271–296 (1985)
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Teng, Z., Xiong, H., Liu, Q. (2014). Unsupervised Joint Monolingual Character Alignment and Word Segmentation. In: Sun, M., Liu, Y., Zhao, J. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2014 2014. Lecture Notes in Computer Science(), vol 8801. Springer, Cham. https://doi.org/10.1007/978-3-319-12277-9_1
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DOI: https://doi.org/10.1007/978-3-319-12277-9_1
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