Joint memory-based learning of syntactic and semantic dependencies in multiple languages

Authors:
Roser Morante;Vincent Van Asch;Antal van den Bosch
Affiliations:
University of Antwerp, Antwerpen, Belgium;University of Antwerp, Antwerpen, Belgium;Tilburg University, Tilburg, The Netherlands
Venue:
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
Year:
2009

Citing 5
Cited 1

Memory-Based Language Processing (Studies in Natural Language Processing)

Memory-Based Language Processing (Studies in Natural Language Processing)
Adding semantic roles to the chinese treebank

Natural Language Engineering
The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies

CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
A combined memory-based semantic role labeler of English

CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
The CoNLL-2009 shared task: syntactic and semantic dependencies in multiple languages

CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task

Learning local content shift detectors from document-level information

EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a system submitted to the CoNLL Shared Task 2009 performing the identification and labeling of syntactic and semantic dependencies in multiple languages. Dependencies are truly jointly learned, i.e. as if they were a single task. The system works in two phases: a classification phase in which three classifiers predict different types of information, and a ranking phase in which the output of the classifiers is combined.