Studying the effect of input size for bayesian word segmentation on the providence corpus

24th International Conference on Computational Linguistics - Proceedings of COLING 2012: Technical Papers

Volumn , Issue , 2012, Pages 325-340

  B̈orschinger, Benjamin ^a,b   Demuth, Katherine ^a   Johnson, Mark ^a  

^a MACQUARIE UNIVERSITY   (Australia)

^b UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

Bayesian modelling; Language acquisition; Resources; Unsupervised learning; Word segmentation

Indexed keywords

BAYESIAN NETWORKS; COMPUTATIONAL METHODS; MODELING LANGUAGES; UNSUPERVISED LEARNING;

BAYESIAN MODELLING; COMPUTATIONAL MODEL; CORPUS-BASED; LANGUAGE ACQUISITION; LONGITUDINAL DATA; RESOURCES; SEGMENTATION PERFORMANCE; WORD SEGMENTATION;

COMPUTATIONAL LINGUISTICS;

EID: 84876808011     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Börschinger, B. and Johnson, M. (2012). Using rejuvenation to improve particle filtering for bayesian word segmentation. In The Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Jeju, South Korea. Association for Computational Linguistics.
2. Boruta, L. and Jastrzebska, J. (2012). A phonemic corpus of polish child-directed speech. In Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12), pages 1017-1020, Istanbul, Turkey. European Language Resources Association (ELRA).
3. Brent, M. (1999). An efficient, probabilistically sound algorithm for segmentation and word discovery. Machine Learning, 34:71-105.
4. Brent, M. and Cartwright, T. (1996). Distributional regularity and phonotactic constraints are useful for segmentation. Cognition, 61:93-125.
5. Demuth, K., Culbertson, J., and Alter, J. (2006). Word-minimality, epenthesis, and coda licensing in the acquisition of english. Language and Speech, 49:137-174.
6. Frank, M. C., Goodman, N., and Tenenbaum, J. (2009). Using speakers' referential intentions to model early cross-situational word learning. Psychological Science, 20:579-585.
7. Gervain, J. and Erra, R. G. (2012). The statistical signature of morphosyntax: A study of hungarian and italian infant-directed speech. Cognition, (0):-.
8. Goldwater, S. (2007). Nonparametric Bayesian Models of Lexical Acquisition. PhD thesis, Brown University.
9. Goldwater, S., Griffiths, T. L., and Johnson, M. (2009). A Bayesian framework for word segmentation: Exploring the effects of context. Cognition, 112(1):21-54.
10. Johnson, M. (2008a). Unsupervised word segmentation for Sesotho using adaptor grammars. In Proceedings of the Tenth Meeting of ACL Special Interest Group on Computational Morphology and Phonology, pages 20-27, Columbus, Ohio. Association for Computational Linguistics.
11. Johnson, M. (2008b). Using adaptor grammars to identifying synergies in the unsupervised acquisition of linguistic structure. In Proceedings of the 46th Annual Meeting of the Association of Computational Linguistics, pages 398-406, Columbus, Ohio. Association for Computational Linguistics.
12. Johnson, M. and Demuth, K. (2010). Unsupervised phonemic Chinese word segmentation using adaptor grammars. In Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), pages 528-536, Beijing, China. Coling 2010 Organizing Committee.
13. Johnson, M. and Goldwater, S. (2009). Improving nonparameteric Bayesian inference: experiments on unsupervised word segmentation with adaptor grammars. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 317-325, Boulder, Colorado. Association for Computational Linguistics.
14. Jones, B. K., Johnson, M., and Frank, M. C. (2010). Learning words and their meanings from unsegmented child-directed speech. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 501-509, Los Angeles, California. Association for Computational Linguistics.
15. Jusczyk, P., Friederici, A., Wessels, J., Svenkerud, V., and marie Jusczyk, A. (1993). Infants' sensitivity to the sound patterns of native language words. Jounral of Memory and Language, 32:402-420.
16. MacWhinney, B. (2000). The CHILDES Project: Tools for analyzing talk, 3rd Edition. Vol 2: The Database. Lawrence Erlbaum Associates.
17. Norris, D., McQueen, J. M., and Cutler, A. (1997). The possible-word constraint in the segmentation of continuous speech. Cognitive Psychology, 34:191-243.
18. Pearl, L., Goldwater, S., and Steyvers, M. (2011). Online learning mechanisms for Bayesian models of word segmentation. Research on Language and Computation, 8(2):107-132.
19. Phillips, L. and Pearl, L. (2012). "Less is more" in Bayesian word segmentation: when cognitively plausible learners outperform the ideal. In Miyake, N., Peebles, D., and Cooper, R. P., editors, Proceedings of the 34th Annual Conference of the Cognitive Science Society, Austin, Texas. Cognitive Science Society.
20. Saffran, J., Aslin, R., and Newport, E. (1996). Statistical learning by 8-month-old infants. Science, 274:1926-1928.
21. Smolensky, P. and Legendre, G. (2005). The Harmonic Mind: From Neural Computation To Optimality-Theoretic Grammar. The MIT Press.
22. Venkataraman, A. (2001). A statistical model for word discovery in transcribed speech. Computational Linguistics, 27(3):351-372.