PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Projective Dependency Parsing with Perceptron
Xavier Carreras, Mihai Surdeanu and Lluis Marquez
In: 10th Conference on Computational Natural Language Learning (CoNLL-X), 8-9 June 2006, New York.

Abstract

We describe an online learning dependency parser for the CoNLL-X Shared Task, based on the bottom-up projective algorithm of Eisner (1996,2000). We experiment with a large feature set that models: the tokens involved in dependencies and their immediate context, the surface-text distance between tokens, and the syntactic context dominated by each dependency. In experiments, the treatment of multilingual information was totally blind.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:2943
Deposited By:Xavier Carreras
Deposited On:26 December 2006