Summary
We propose a method for generating grammars for natural language that applies an inductive learning algorithm to large corpora. This learning procedure incrementally parses the corpora using a linguistically motivated basic grammar. For structures not describable by this grammar, rule hypotheses are generated, evaluated, and finally integrated into the grammar. Both statistical information and linguistic constraints are employed during the generation and evaluation process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
ANGLUIN, D. (1980): Inductive inference of formal languages from positive data. Information and Control, 45, 117–135.
BERWICK, R. C. (1985): The Acquisition of Syntactic Knowledge. MIT Press, Cambridge, Massachusetts.
COOK, C. M.; ROSENFELD, A. and ARONSON, A. R. (1976): Grammatical inference by hill-climbing. Informational Sciences, 10, 59–80.
GIORDANO, J.-Y. (1993): Version space for learning context-free grammars. Grammatical Inference: Theory, Applications ans Alternatives, 17-25.
GOLD, E. M. (1967): Language identification in the limit. Information and Control, 10, 447–474.
NAUMANN, S. and SCHREPP, J. (1992): An empirical approach to syntax learning. Proceedings of KONVENS 1992, 209-217.
OSBORNE, M. (1994): Learning unification-based natural language grammars. PhD thesis, University of York.
VANLEHN, K. and BALL, W. (1987): A version space approach to learning context-free grammars. Machine Learning, 2, 39–74.
WOLFF, J. G. (1991): Towards a Theory of Cognition and Computing. Ellis Horwood, New York.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Naumann, S., Gieseking, K. (1997). Deriving Grammars from Large Corpora. In: Klar, R., Opitz, O. (eds) Classification and Knowledge Organization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59051-1_68
Download citation
DOI: https://doi.org/10.1007/978-3-642-59051-1_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-62981-8
Online ISBN: 978-3-642-59051-1
eBook Packages: Springer Book Archive