Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/78183
Title: Improved combinatory categorial grammar induction with boundary words and bayesian inference
Authors: Huang, Y.
Zhang, M.
Tan, C.L. 
Keywords: Bayesian model
Boundary words
Combinatory categorial grammar
Grammar induction
Issue Date: 2012
Source: Huang, Y.,Zhang, M.,Tan, C.L. (2012). Improved combinatory categorial grammar induction with boundary words and bayesian inference. 24th International Conference on Computational Linguistics - Proceedings of COLING 2012: Technical Papers : 1257-1274. ScholarBank@NUS Repository.
Abstract: Combinatory Categorial Grammar (CCG) is an expressive grammar formalism which is able to capture long-range dependencies. However, building large and wide-coverage treebanks for CCG is expensive and time-consuming. In this paper, we focus on the problem of unsupervised CCG induction from plain texts. Based on the baseline model in (Bisk and Hockenmaier, 2012), we propose following two improvements: (1) we utilize boundary part-of-speech (POS) tags to capture lexical information; (2) we perform nonparametric Bayesian inference based on the Pitman-Yor process to learn compact grammars. Experiments on English Penn treebank demonstrate the effectiveness of our boundary model and Bayesian learning. © 2012 The COLING.
Source Title: 24th International Conference on Computational Linguistics - Proceedings of COLING 2012: Technical Papers
URI: http://scholarbank.nus.edu.sg/handle/10635/78183
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