Learning syntactic structures with XML

Authors:
Hervé Déjean
Affiliations:
Universität Tübingen
Venue:
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Year:
2000

Citing 0
Cited 4

Memory-based shallow parsing

The Journal of Machine Learning Research
Shallow parsing using specialized hmms

The Journal of Machine Learning Research
Introduction to the CoNLL-2000 shared task: chunking

ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Learning computational grammars

ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present the result of a symbolic machine learning system for the CoNLL-2000 shared task. This system, ALLiS, is based on theory refinement. In this paper we want to show that XML format not only offers a good framework to annotate texts, but also provides a good formalism and tools in order to learn (syntactic) structures.