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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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.
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