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: RANLP 2009, 14-16 September, Borovets, Bulgaria.

Abstract

We present a comparison between two systems for establishing syntactic and semantic dependencies: one that performs dependency parsing and semantic role labeling as a single task, and another that performs the two tasks in isolation. The systems are based on local memory-based classifiers predicting syntactic and semantic 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 classifiers is combined per sentence into a dependency graph and semantic role labeling assignments for all predicates. The comparison shows that in the learning phase a joint approach produces better-scoring classifiers, 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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:5758
Deposited By:Roser Morante
Deposited On:08 March 2010