Beyond the pipeline: Discrete optimization in NLP

CoNLL 2005 - Proceedings of the Ninth Conference on Computational Natural Language Learning

Volumn , Issue , 2005, Pages 136-143

  Marciniak, Tomasz ^a   Strube, Michael ^a  

^a EML Research gGmbH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CASCADE OF CLASSIFIERS; DISCRETE OPTIMIZATION; LANGUAGE PROCESSING SYSTEMS; LINEAR PROGRAMMING FORMULATION; OPTIMAL SOLUTIONS; SEQUENTIAL PROCESSING;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84862276326     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Althaus, E., N. Karamanis & A. Koller (2004). Computing locally coherent discourses. In Proceedings of the 42 Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain, July 21-26, 2004, pp. 399.406.
2. Buchholz, S., J. Veenstra & W. Daelemans (1999). Cascaded grammatical relation assignment. In Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, College Park, Md., June 21-22, 1999, pp. 239.246.
3. Chekuri, C., S. Khanna, J. Naor & L. Zosin (2001). Approximation algorithms for the metric labeling problem via a new linear programming formulation. In Proceedings of the 12th Annual ACM SIAM Symposium on Discrete Algorithms, Washington, DC, pp. 109.118.
4. Cunningham, H., K. Humphreys, Y. Wilks & R. Gaizauskas (1997). Software infrastructure for natural language processing. In Proceedings of the Fifth Conference on Applied Natural Language Processing Washington, DC, March 31-April 3, 1997, pp. 237.244.
5. Daelemans, W. & A. van den Bosch (1998). Rapid development of NLP modules with memory-based learning. In Proceedings of ELSNET in Wonderland. Utrecht: ELSNET, pp. 105.113.
6. Edwards, Allen, L. (1976). An Introduction to Linear Regression and Correlation. San Francisco, Cal.: W. H. Freeman.
7. Goodman, L. A. & W. H. Kruskal (1972). Measures of association for cross-classi_cation, iv. Journal of the American Statistical Association, 67:415.421.
8. Kleinberg, J. M. & E. Tardos (2000). Approximation algorithms for classi_cation problems with pairwise relationships: Metric labeling and Markov random _elds. Journal of the ACM, 49(5):616.639.
9. Kohavi, R. & G. H. John (1997). Wrappers for feature subset selection. Arti_cial Intelligence Journal, 97:273.324.
10. Marciniak, T. & M. Strube (2004). Classi_cation-based generation using TAG. In Proceedings of the 3rd International Conference on Natural Language Generation, Brockenhurst, UK, 14-16 July, 2004, pp. 100.109.
11. Marciniak, T. & M. Strube (2005). Modeling and annotating the semantics of route directions. In Proceedings of the 6th International Workshop on Computational Semantics, Tilburg, The Netherlands, January 12-14, 2005, pp. 151.162.
12. Nemhauser, G. L. & L. A. Wolsey (1999). Integer and combinatorial optimization. New York, NY: Wiley.
13. Punyakanok, V., D. Roth, W. Yih & Z. Dav (2004). Semantic role labeling via integer linear programming inference. In Proceedings of the 20th International Conference on Computational Linguistics, Geneva, Switzerland, August 23-27, 2004, pp. 1346.1352.
14. Reiter, E. (1994). Has a consensus NL generation architecture appeared, and is it psycholinguistically plausible? In Proceedings of the 7th International Workshop on Natural Language Generation, Kennebunkport, Maine, pp. 160.173.
15. Reiter, E. & R. Dale (2000). Building Natural Language Generation Systems. Cambridge, UK: Cambridge University Press.
16. Roth, D. & W. Yih (2004). A linear programming formulation for global inference in natural language tasks. In Proceedings of the 8th Conference on Computational Natural Language Learning, Boston, Mass., May 2-7, 2004, pp. 1.8.
17. Siegel, S. & N. J. Castellan (1988). Nonparametric Statistics for the Behavioral Sciences. New York, NY: McGraw-Hill.
18. Soon, W. M., H. T. Ng & D. C. L. Lim (2001). A machine learning approach to coreference resolution of noun phrases. Computational Linguistics, 27(4):521.544.
19. Van den Bosch, A., T. Weijters &W. Daelemans (1998). Modularity in inductively-learned word pronunciation systems. In D. Powers (Ed.), Proceedings of NeMLaP3/CoNLL98, pp. 185.194.
20. Witten, I. H. & E. Frank (2000). Data Mining-Practical Machine Learning Tools and Techniques with Java Implementations. San Francisco, Cal.: Morgan Kaufmann.