Abstract
Incremental parsing is appealing for applications such as speech recognition and machine translation due to its inherent efficiency as well as being a natural match for the language models commonly used in such systems. In this paper we introduce an Incremental Combinatory Categorical Grammar (ICCG) that extends the standard CCG grammar to enable fully incremental left-to-right parsing. Furthermore, we introduce a novel dynamic programming algorithm to convert CCGbank normal form derivations to incremental left-to-right derivations and show that our incremental CCG derivations can recover the unlabeled predicate-argument dependency structures with more than 96% F-measure. The introduced CCG incremental derivations can be used to train an incremental CCG parser.
This work was conducted while the first two authors were at IBM Cairo Technology Development Center.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Clark, S., Curran, J.R.: Wide-coverage efficient statistical parsing with CCG and log-linear models. Comput. Linguist. 33(4), 493–552 (2007)
Hassan, H., Sima’an, K., Way, A.: Lexicalized semi-incremental dependency parsing. In: Proceedings of RANLP 2009 (2009)
Hassan, H., Sima’an, K., Way, A.: A syntactified direct translation model with linear-time decoding. In: EMNLP 2009: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp. 1182–1191. Association for Computational Linguistics, Morristown (2009)
Hockenmaier, J., Steedman, M.: CCGbank: A corpus of CCG derivations and dependency structures extracted from the Penn Treebank. Comput. Linguist. 33(3), 355–396 (2007)
Nivre, J.: Incrementality in deterministic dependency parsing. In: IncrementParsing 2004: Proceedings of the Workshop on Incremental Parsing, pp. 50–57. Association for Computational Linguistics, Morristown (2004)
Nivre, J.: Algorithms for deterministic incremental dependency parsing. Comput. Linguist. 34(4), 513–553 (2008)
Sagae, K., Lavie, A.: A best-first probabilistic shift-reduce parser. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, pp. 691–698. Association for Computational Linguistics, Morristown (2006)
Schuler, W.: Positive results for parsing with a bounded stack using a model-based right-corner transform. In: NAACL 2009: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 344–352. Association for Computational Linguistics, Morristown (2009)
Schuler, W., Miller, T., AbdelRahman, S., Schwartz, L.: Toward a psycholinguistically-motivated model of language processing. In: COLING 2008: Proceedings of the 22nd International Conference on Computational Linguistics, pp. 785–792. Association for Computational Linguistics, Morristown (2008)
Steedman, M.: The Syntactic Process. MIT Press, Cambridge (2000)
Yamada, H., Matsumoto, Y.: Statistical dependency analysis with support vector machines. In: Proceedings of IWPT, pp. 195–206 (2003)
Zettlemoyer, L.S., Collins, M.: Online learning of relaxed CCG grammars for parsing to logical form. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2007), pp. 678–687 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hefny, A., Hassan, H., Bahgat, M. (2011). Incremental Combinatory Categorial Grammar and Its Derivations. In: Gelbukh, A.F. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19400-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-19400-9_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19399-6
Online ISBN: 978-3-642-19400-9
eBook Packages: Computer ScienceComputer Science (R0)