Publishing Partner: Cambridge University Press CUP Extra Publisher Login
amazon logo
More Info


New from Oxford University Press!

ad

Words in Time and Place: Exploring Language Through the Historical Thesaurus of the Oxford English Dictionary

By David Crystal

Offers a unique view of the English language and its development, and includes witty commentary and anecdotes along the way.


New from Cambridge University Press!

ad

The Indo-European Controversy: Facts and Fallacies in Historical Linguistics

By Asya Pereltsvaig and Martin W. Lewis

This book "asserts that the origin and spread of languages must be examined primarily through the time-tested techniques of linguistic analysis, rather than those of evolutionary biology" and "defends traditional practices in historical linguistics while remaining open to new techniques, including computational methods" and "will appeal to readers interested in world history and world geography."


Academic Paper


Title: Using Machine-Learning to Assign Function Labels to Parser Output for Spanish
Paper URL: http://acl.ldc.upenn.edu/P/P06/P06-2018.pdf
Author: Grzegorz ChrupaƂa
Email: click here TO access email
Homepage: http://www.lsv.uni-saarland.de/personalPages/gchrupala/index.html
Institution: Saarland University
Author: Josef Van Genabith
Email: click here TO access email
Institution: Dublin City University
Linguistic Field: Computational Linguistics
Subject Language: Spanish
Spanish
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 achieve an f-score of 75.67%, a statistically significant 3.4% improvement over the baseline.
Type: Individual Paper
Status: Completed
Publication Info: Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions
URL: http://acl.ldc.upenn.edu/P/P06/P06-2018.pdf


Add a new paper
Return to Academic Papers main page
Return to Directory of Linguists main page