LAPIS (CTU+York)

System LAPIS
Version 1.2
Code SICStus Prolog
References [2}, [3], [1}
Pointers kazakov@cs.york.ac.uk, step@labe.felk.cvut.cz
Other comments temporarily restricted availability

The system LAPIS implements an approach to learning LR parsers from tree-banks annotated with lexical semantic labels. The parsers so learned are based on grammars which are adapted to obtain optimal performance on the training data, i.e. to reduce the number of spurious parses by means of rule unfolding, lexicalisation, and the use of lexical semantic constraints. Such tailored grammars are extremely useful for restricted domains but a new one must be written for each of them, and their manual development can become very costly.

The fact that in LAPIS the concepts learned are parser actions rather than grammar rules, has a clear justification. The major possible criticism related to the difficulty to interpret the results obtained when parser actions are learned, is easily rejected, as the mapping of parser actions into grammar rules is straight-forward. In that way, the results of learning, the specialisations of parser actions are easily interpreted as partially unfolded and/or lexicalised grammar rules also containing lexical semantic constraints. One the other hand, although grammar rules and parser actions are closely related, the former are recursive concepts, whereas the latter are not. Learning recursive concepts is a notoriously hard task, therefore the shift in paradigm from learning grammar rules to learning parser actions allows to reduce the complexity of learning.

The integrated framework for speech recognition and parsing described in [1] traces the way of a number of possible applications in which the LR parsers learned with LAPIS are combined with the IBM commercial tools for speech recognition. An additional advantage of LAPIS in this context is the fact that the SRCL grammars required by the SR tools can be developed automatically, as a by-product of the parser learning.

References

  1. L. Jelinek, D. Kazakov, K. Maly, and 0. Stepankova. Speech support for control. In ISMCR'98 (TC17) Proceedings of the Eighth International Symposium on Measurement and Control in Robotics, pp. 271-277. ISBN 80-01-01814-8, Czech National Committee IMEKO/CTU Prague, 1998.

  2. Dimitar Kazakov. Natural Language Processing Applications of Machine Learning. PhD thesis, Department of Cybernetics, Czech Technical University, Prague, May 1999.

  3. D. Kazakov. Combining LAPIS and WordNet for the Learning of LR Parsers with Optimal Semantic Constraints. In S. Dzeroski and P. Flach, editors, Proceedings of the 9th International Workshop on Inductive Logic Programming, volume 1634 of Lecture Notes in Artificial Intelligence, pages 140--151. Springer-Verlag, 1999.


back to index