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Using machine-learning to assign function labels to parser output for Spanish

Chrupała, Grzegorz and van Genabith, Josef (2006) Using machine-learning to assign function labels to parser output for Spanish. In: COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, 17-21 July 2006, Sydney, Australia.

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Abstract

Data-driven grammatical function tag assignment has been studied for English using the Penn-II Treebank data. In this paper we address the question of whether such methods can be applied successfully to other languages and treebank resources. In addition to tag assignment accuracy and f-scores we also present results of a task-based evaluation. We use three machine-learning methods to assign Cast3LB function tags to sentences parsed with Bikel’s parser trained on the Cast3LB treebank. The best performing method, SVM, achieves an f-score of 86.87% on gold-standard trees and 66.67% on parser output - a statistically significant improvement of 6.74% over the baseline. In a task-based evaluation we generate LFG functional-structures from the function tag-enriched trees. On this task we achive an f-score of 75.67%, a statistically significant 3.4% improvement over the baseline.

Item Type:Conference or Workshop Item (Poster)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Spanish;
Subjects:Computer Science > Machine translating
Computer Science > Machine learning
DCU Faculties and Centres:Research Initiatives and Centres > National Centre for Language Technology (NCLT)
Publisher:Association for Computational Linguistics
Official URL:http://www.aclweb.org/anthology/P/P06/
Copyright Information:© 2006 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:15270
Deposited On:10 Mar 2010 13:31 by DORAS Administrator. Last Modified 27 Apr 2010 16:55

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