Learning semantic correspondences with less supervision

ACL-IJCNLP 2009 - Joint Conf. of the 47th Annual Meeting of the Association for Computational Linguistics and 4th Int. Joint Conf. on Natural Language Processing of the AFNLP, Proceedings of the Conf.

Volumn , Issue , 2009, Pages 91-99

  Liang, Percy ^a   Jordan, Michael I ^a   Klein, Dan ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; COMPUTER HARDWARE DESCRIPTION LANGUAGES; SEMANTICS; WEATHER FORECASTING;

CENTRAL PROBLEMS; GENERATIVE MODEL; LANGUAGE ACQUISITION; LEARNING SEMANTICS; ROBOCUP;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 80053391310     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1687878.1687893     Document Type: Conference Paper

References (20)

1. R. Barzilay and M. Lapata. 2005. Collective content selection for concept-to-text generation. In Human Language Technology and Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 331-338, Vancouver, B.C.
2. R. Barzilay and M. Lapata. 2008. Modeling local coherence: An entity-based approach. Computational Linguistics, 34:1-34.
3. P. F. Brown, S. A. D. Pietra, V. J. D. Pietra, and R. L. Mercer. 1993. The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19:263-311.
4. D. L. Chen and R. J. Mooney. 2008. Learning to sportscast: A test of grounded language acquisition. In International Conference on Machine Learning (ICML), pages 128-135. Omnipress.
5. J. DeNero, A. Bouchard-Côté, and D. Klein. 2008. Sampling alignment structure under a Bayesian translation model. In Empirical Methods in Natural Language Processing (EMNLP), pages 314-323, Honolulu, HI.
6. J. Eisenstein and R. Barzilay. 2008. Bayesian unsupervised topic segmentation. In Empirical Methods in Natural Language Processing (EMNLP), pages 334-343.
7. J. Feldman and S. Narayanan. 2004. Embodied meaning in a neural theory of language. Brain and Language, 89:385-392.
8. R. Ge and R. J. Mooney. 2005. A statistical semantic parser that integrates syntax and semantics. In Computational Natural Language Learning (CoNLL), pages 9-16, Ann Arbor, Michigan.
9. P. Gorniak and D. Roy. 2007. Situated language understanding as filtering perceived affordances. Cognitive Science, 31:197-231.
10. T. Grenager, D. Klein, and C. D. Manning. 2005. Unsupervised learning of field segmentation models for information extraction. In Association for Computational Linguistics (ACL), pages 371-378, Ann Arbor, Michigan. Association for Computational Linguistics.
11. R. J. Kate and R. J. Mooney. 2007. Learning language semantics from ambiguous supervision. In Association for the Advancement of Artificial Intelligence (AAAI), pages 895-900, Cambridge, MA. MIT Press.
12. P. Liang, B. Taskar, and D. Klein. 2006. Alignment by agreement. In North American Association for Computational Linguistics (NAACL), pages 104-111, New York City. Association for Computational Linguistics.
13. W. Lu, H. T. Ng, W. S. Lee, and L. S. Zettlemoyer. 2008. A generative model for parsing natural language to meaning representations. In Empirical Methods in Natural Language Processing (EMNLP), pages 783-792.
14. R. C. Moore. 2004. Improving IBM word alignment model 1. In Association for Computational Linguistics (ACL), pages 518-525, Barcelona, Spain. Association for Computational Linguistics.
15. H. Ney and S. Vogel. 1996. HMM-based word alignment in statistical translation. In International Conference on Computational Linguistics (COLING), pages 836-841. Association for Computational Linguistics.
16. J. M. Siskind. 1996. A computational study of crosssituational techniques for learning word-to-meaning mappings. Cognition, 61:1-38.
17. B. Snyder and R. Barzilay. 2007. Database-text alignment via structured multilabel classification. In International Joint Conference on Artificial Intelligence (IJCAI), pages 1713-1718, Hyderabad, India.
18. C. Yu and D. H. Ballard. 2004. On the integration of grounding language and learning objects. In Association for the Advancement of Artificial Intelligence (AAAI), pages 488-493, Cambridge, MA. MIT Press.
19. L. S. Zettlemoyer and M. Collins. 2005. Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars. In Uncertainty in Artificial Intelligence (UAI), pages 658-666.
20. L. S. Zettlemoyer and M. Collins. 2007. Online learning of relaxed CCG grammars for parsing to logical form. In Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL), pages 678-687.