Arturo Trujillo
ABSTRACT
This paper presents a pruning technique which can be used to reduce the number of paths searched in rule-based bag generators of the type proposed by (Poznański et al., 1995) and (Popowich, 1995). Pruning the search space in these generators is important given the computational cost of bag generation. The technique relies on a connetivity constraint between the semantic indices associated with each lexical sign in a bag. Testing the algorithm on a range of sentences shows reductions in the generation time and the number of edges constructed.
- A. V. Aho, R. Sethi, and J. D. Ullman. 1986. Compilers-Principles, Techniques, and Tools. Addison Wesley, Reading, MA. Google Scholar
Digital Library
- J. L. Beaven. 1992. Lexicalist Unification Based Machine Translation. Ph.D. thesis, Department of Artificial Intelligence, University of Edinburgh, Edinburgh, UK.Google Scholar
- C. Brew. 1992. Letting the cat out of the bag: Generation for Shake-and-Bake MT. In Proceedings of the 14th COLING, pages 610--16, Nantes, France, August. Google ScholarDigital Library
- J. Calder, M. Reape, and H. Zeevat. 1989. An algorithm for generation in unification categorial grammar. In Proceedings of the Fourth European Conference of the ACL, pages 233--40, Manchester, England, April. Google ScholarDigital Library
- H. H. Chen and Y. S. Lee. 1994. A corrective training algorithm for adaptive learning in bag generation. In New Methods in Language Processing, Manchester, UK.Google Scholar
- A. Copestake, D. Flickinger, R. Malouf, S. Riehemann, and I. Sag. 1995. Translation using minimal recursion semantics. In Proceedings of the 6th International Conference on Theoretical and Methodological Issues in Machine Translation, Leuven, Belgium, July.Google Scholar
- H. Kamp and U. Reyle. 1993. From Discourse to Logic-Introduction to Modeltheoretic Semantics of Natural Language, Formal logic and Discourse Representation Theory, volume 42 of Studies in Linguistics and Philosophy. Kluwer Academic, Dordrecht, The Netherlands.Google Scholar
- Ken Kennedy. 1981. A survey of data flow analysis techniques. In Muchnick and Jones (1981), chapter 1, pages 5--54.Google Scholar
- Steven S. Muchnick and Neil D. Jones, editors. 1981. Program Flow Analysis: Theory and Applications. Software. Prentice-Hall, Englewood Cliffs, NJ. Google ScholarDigital Library
- J. D. Phillips. 1993. Generation of text from logical formulae. Machine Translation, 8(4):209--35.Google ScholarCross Ref
- C. Pollard and I. Sag. 1994. Head Driven Phrase Structure Grammar. Chicago University Press, IL.Google Scholar
- Fred Popowich. 1995. Improving the efficiency of a generation algorithm for Shake and Bake machine translation using Head-Driven Phrase Structure Grammar. In Proceedings of Natural Language Understanding and Logic Programming V, Lisbon, Portugal, May.Google Scholar
- V. Poznański, J. L. Beaven, and P. Whitelock. 1995. An efficient generation algorithm for lexicalist MT. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, Boston, MA, June. Google ScholarDigital Library
- Uwe Reyle. 1995. On reasoning with ambiguities. In Proceedings of the Seventh Conference of the European Chapter of the Association for Computational Linguistics, pages 1--15, Dublin, Ireland, March. Google ScholarDigital Library
- C. J. Rupp, M. A. Rosner, and R. L. Johnson, editors. 1994. Constraints, Language and Computation. Academic Press, London.Google Scholar
- S. M. Shieber. 1986. An Introduction to Unification-based Approaches to Grammar, volume 4 of CSLI Lecture Notes. CSLI, Standford, CA.Google Scholar
- A. Trujillo. 1994. Computing FIRST and FOLLOW functions for Feature-Theoretic grammars. In Proceedings of the 15th COLING, pages 875--80, Kyoto, Japan, August. Google ScholarDigital Library
- A. Trujillo. 1995. Lexicalist Machine Translation of Spatial Prepositions. Ph.D. thesis, Computer Laboratory, University of Cambridge, April.Google Scholar
- Pete Whitelock. 1994. Shake-and-bake translation. In Rupp et al. (1994), pages 339--59.Google Scholar
- Connectivity in bag generation
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