Combining LAPIS and WordNet for the Learning
of LR Parsers with Optimal Semantic
Constraints

Dimitar Kazakov

University of York, Heslington, York YO1O 5DD, UK
kazakov@cs.york.ac.uk,
WWW home page: http://www.cs.york.ac.uk/~kazakov/



Abstract. There is a history of research focussed on learning of shift-reduce 
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.
References

1.	Alfred Aho, Ravi Sethi, and Jeffrey Uliman. Compilateurs - Principles, techniques
et outils. InterEditions, Paris, 1989.
2.	George A. Miller et al. Introduction to WordNet: An on-line lexical database.
Technical report, University of Princeton, 1993.
3.	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.
4.	Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. Building a
large annotated corpus of English: the Penn treebank. Computational Linguistics,
19, 1993.
5.	Tom M. Mitchell. Machine Learning. McGraw-Hill, 1997.
6.	G. Plotkin. A note of inductive generalization. In B. Meltzer and D. Mitchie, editors,
Machine Intelligence 5, pages 153163. Edinburgh University Press, 1970.
7.	Ch. Samuelsson. Fast NaturalLanguage Parsing Using ExplanationBased Learning. 
PhD thesis, The Royal Institute of Technology and Stockholm University, 1994.
8.	John M. Zelle. Using Inductive Logic Programming to Automate the Construction
of Natural Language Parsers. PhD thesis, The University of Texas at Austin, 1995.
9.	John M. Zelle and Raymond J. Mooney. Inducing deterministic Prolog parsers from
treebanks: A machine learning approach. In Proceedings of AAAI-94, pages 748753.
AAI Press/MIT Press, 1994.
