PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Dependency Parsing and Semantic Role Labeling as a Single Task
Roser Morante, Vincent Van Asch and Antal Van den Bosch
In: 7th International Conference on Recent Advances in Natural Language Processing, 14-16 Sep 2009, Borovets, Bulgaria.

Abstract

We present a comparison between two systems for establishing syntactic and semantic depen- dencies: one that performs dependency parsing and semantic role labeling as a single task, and another that performs the two tasks in isola- tion. The systems are based on local memory- based classiers predicting syntactic and seman- tic dependency relations between pairs of words. In a second global phase, the systems perform a deterministic ranking procedure in which the output of the local classiers is combined per sentence into a dependency graph and seman- tic role labeling assignments for all predicates. The comparison shows that in the learning phase a joint approach produces better-scoring classi- ers, while after the ranking phase the isolated approach produces the most accurate syntactic dependencies, while the joint approach yields the most accurate semantic role assignments.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:5687
Deposited By:Vincent Van Asch
Deposited On:08 March 2010