Study on Disambiguation by Statistical and Logical Reasoning

Article Preview

Abstract:

In order to find an effective for the disambiguation, we explore the ways of complementing statistical approaches with the use of ‘domain theories’, and suppose that disambiguation decisions can supply tacit information about such theories, and the theories can be in part automatically induced from such data. The experiment results can be used successfully in disambiguating other sentences from the same domain.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 108-111)

Pages:

1080-1085

Citation:

Online since:

May 2010

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2010 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] J. R. Hobbs, M. E. Stickel, D. E. Appelt, and P. Martin. Interpretation as abduction. Artificial Intelligence, Vol. 63, (1993), p.69.

DOI: 10.1016/0004-3702(93)90015-4

Google Scholar

[2] S. Muggleton and L De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, Vol. 19, 20, (1994), p.629.

DOI: 10.1016/0743-1066(94)90035-3

Google Scholar

[3] S. Muggleton. Inverse entailment and Progol. New Generation Computing, Vol. 13, (1995), p.245.

DOI: 10.1007/bf03037227

Google Scholar

[4] S.G. Pulman. Unification encodings of grammatical notations. Computational Linguistics, 22/3: 295-328, (1996).

Google Scholar

[5] R. Manthey and F. Bry. Satchmo: a theorem prover implemented in Prolog. In CADE 88: 9th Conference on Automated Deduction, pages 415-434. Lecture Notes in Computer Science, Springer Verlag, (1988).

DOI: 10.1007/bfb0012847

Google Scholar