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Three-Phase English Syntactic Analysis for Improving the Parsing Efficiency
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 Title & Authors
Three-Phase English Syntactic Analysis for Improving the Parsing Efficiency
Kim, Sung-Dong;
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 Abstract
The performance of an English-Korean machine translation system depends heavily on its English parser. The parser in this paper is a part of the rule-based English-Korean MT system, which includes many syntactic rules and performs the chart-based parsing. The parser generates too many structures due to many syntactic rules, so much time and memory are required. The rule-based parser has difficulty in analyzing and translating the long sentences including the commas because they cause high parsing complexity. In this paper, we propose the 3-phase parsing method with sentence segmentation to efficiently translate the long sentences appearing in usual. Each phase of the syntactic analysis applies its own independent syntactic rules in order to reduce parsing complexity. For the purpose, we classify the syntactic rules into 3 classes and design the 3-phase parsing algorithm. Especially, the syntactic rules in the 3rd class are for the sentence structures composed with commas. We present the automatic rule acquisition method for 3rd class rules from the syntactic analysis of the corpus, with which we aim to continuously improve the coverage of the parsing. The experimental results shows that the proposed 3-phase parsing method is superior to the prior parsing method using only intra-sentence segmentation in terms of the parsing speed/memory efficiency with keeping the translation quality.
 Keywords
3-phase Syntactic Analysis;English-Korean Machine Translation;Intra-Sentence Segmentation;Rule-Based Machine Translation;Automatic Syntactic Rule Acquisition;
 Language
Korean
 Cited by
 References
1.
Sung-Dong Kim, Byoung-Tak Zhang, and Yung Taek Kim, "Learning-based Intrasentence Segmentation for Efficient Parsing of Long Sentences," Journal of Machine Translation, Vol.16, No.3, pp.151-174, 2001. crossref(new window)

2.
Sung-Dong Kim, "Intra-Sentence Segmentation using Maximum Entropy Model for Efficient Parsing of English Sentences," Journal of Korean Institute of Information Science and Engineering, Vol.32, No.5, 2005.

3.
Hye-Kyum Kim, Kyung-Mi Park, Yeo-Chan Yoon, Hae- Chang Rim, and So-Young Park, "Tree Tagging Tool using Two-phrase Parsing," Proceedings of the 17th Annual Conference on Human & Cognitive Language Technology (HCLT 2005), 2005.

4.
M. Jin, M.-Y. Kim, and J.-H. Lee, "Two-Phase Shift-Reduce Deterministic De pendency Parser of Chinese," in Proceedings of the 2nd International Joint Conference on Natural Language Processing (IJCNLP), 2005.

5.
Joseph Turial, "Constituent Parsing By Classification," PhD dissertation, Computer Science Department, Sept., 2007.

6.
Xiao Chen, "Discriminative Constituent Parsing with Localized Features," PhD thesis, City University of Hong Kong, 2012.

7.
J. Nivre and M. Scholz, "Deterministic dependency parsing of English text," in Proceedings of the 20th International Conference on Computational Linguistics, pp.64-70, Geneva, Switzerland, 2004.

8.
A. Michael and A. Covington, "Fundamental Algorithm for Dependency Parsing," in Proceedings of the 39th Annual ACM Southeast Conference, ed. John A. Miller and Jeffrey W. Smith, pp.95-102, 2001.

9.
L. Banarescu et. al., "Abstract meaning representation for sembanking," in Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pp.178-186, 2013.

10.
J. Flanigan et. al., "A discriminative graph-based parser for the abstract meaning representation," in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp.1426-1436, 2014.

11.
C. Wang et. al., "A transition-based algorithm for amr parsing," in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics, pp.366-375, 2015.

12.
M. Pust et. al., "Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation," in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp.1143-1154, 2015.

13.
An International Handbook of Contemporary Research, Edited by V. Agel, L.M. Eichinger, H.-W. Eroms, P. Hellwig, H.-J. Heringer, H. Lobin. Volume II. pp. 1081-1108. Mouton: 2006.

14.
S. Abney, "Part-of-Speech Tagging and Partial Parsing," Corpus-Based Methods in Language and Speech, pp.118-136, 1996.

15.
B. Srinivas, "A lightweight dependency analyzer for partial parsing," Natural Language Engineering, Vol.6, No.2, pp. 113-138, 2000. crossref(new window)

16.
H. Faili, "From Partial toward Full Parsing," Proceedings of the International Conference on Recent Advances on Natural Language Processing 2009, pp.71-75, 2009.

17.
Y. Oda et. al., "Optimizing Segmentation Strategies for Simultaneous Speech Recognition," Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp.551-556, 2014.

18.
Sung-Dong Kim, "English Syntactic Rule Management System for Rule-Based English-Korean Machine Translation System," KIISE(Korean Institute of Information Science and Engineering) Transactions on Computing Practice, Vol.20, No.7, pp.398-407, 2014.

19.
Sung-Dong Kim, "Intra-sentence Segmentation using Finite Automata for Efficient English Syntactic Analysis," KIISE(Korean Institute of Information Science and Engineering) Transactions on Computing Practices, Vol.19, No.4, pp.186-193, 2013.