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Recovering non-local dependencies for Chinese

Guo, Yuqing, Wang, Haifeng and van Genabith, Josef (2007) Recovering non-local dependencies for Chinese. In: EMNLP-CoNLL 2007 - Joint Meeting of the Conference on Empirical Methods in Natural Language Processing and the Conference on Computational Natural Language Learning, 28-30 June 2007, Prague, Czech Republic.

Abstract
To date, work on Non-Local Dependencies (NLDs) has focused almost exclusively on English and it is an open research question how well these approaches migrate to other languages. This paper surveys non-local dependency constructions in Chinese as represented in the Penn Chinese Treebank (CTB) and provides an approach for generating proper predicate-argument-modifier structures including NLDs from surface contextfree phrase structure trees. Our approach recovers non-local dependencies at the level of Lexical-Functional Grammar f-structures, using automatically acquired subcategorisation frames and f-structure paths linking antecedents and traces in NLDs. Currently our algorithm achieves 92.2% f-score for trace insertion and 84.3% for antecedent recovery evaluating on gold-standard CTB trees, and 64.7% and 54.7%, respectively, on CTBtrained state-of-the-art parser output trees.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Chinese language;
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:Research Institutes and Centres > National Centre for Language Technology (NCLT)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:Association for Computational Linguistics
Official URL:http://www.aclweb.org/anthology/D/D07/
Copyright Information:© 2007 Association for Computational Linguistics
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland, SFI 04/IN/I527
ID Code:15211
Deposited On:17 Feb 2010 16:34 by DORAS Administrator . Last Modified 19 Jul 2018 14:50
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