Abstract
There is a history of research focussed on learning of shiftreduce parsers from syntactically annotated corpora by the means of machine learning techniques based on logic. The presence of lexical semantic tags in the treebank has proved useful for learning semantic constraints which limit the amount of nondeterminism in the parsers. The level of generality of the semantic tags used is of direct importance to that task. We combine the ILP system LAPIS with the lexical resource WordNet to learn parsers with semantic constraints. The generality of these constraints is automatically selected by LAPIS from a number of options provided by the corpus annotator. The performance of the parsers learned is evaluated on an original corpus also described in the article.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Alfred Aho, Ravi Sethi, and Jeffrey Ullman. Compilateurs-Principles, techniques et outils. InterEditions, Paris, 1989.
George A. Miller et al. Introduction to WordNet: An on-line lexical database. Technical report, University of Princeton, 1993.
Dimitar Kazakov. An inductive approach to natural language parser design. In Kemal Oflazer and Harold Somers, editors, Proceedings of NeMLaP-2, pages 209–217, Ankara, 1996. Bilkent University.
Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. Building a large annotated corpus of English: the Penn treebank. Computational Linguistics, 19, 1993.
Tom M. Mitchell. Machine Learning. McGraw-Hill, 1997.
G. Plotkin. A note of inductive generalization. In B. Meltzer and D. Mitchie, editors, Machine Intelligence 5, pages 153–163. Edinburgh University Press, 1970.
Ch. Samuelsson. Fast Natural-Language Parsing Using Explanation-Based Learning. PhD thesis, The Royal Institute of Technology and Stockholm University, 1994.
John M. Zelle. Using Inductive Logic Programming to Automate the Construction of Natural Language Parsers. PhD thesis, The University of Texas at Austin, 1995.
John M. Zelle and Raymond J. Mooney. Inducing deterministic Prolog parsers from treebanks: A machine learning approach. In Proceedings of AAAI-94, pages 748–753. AAI Press/MIT Press, 1994.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kazakov, D. (1999). Combining LAPIS and WordNet for the Learning of LR Parsers with Optimal Semantic Constraints. In: Džeroski, S., Flach, P. (eds) Inductive Logic Programming. ILP 1999. Lecture Notes in Computer Science(), vol 1634. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48751-4_14
Download citation
DOI: https://doi.org/10.1007/3-540-48751-4_14
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66109-2
Online ISBN: 978-3-540-48751-7
eBook Packages: Springer Book Archive