Topic Shift in Efficient Discourse Production

Expanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011

Volumn , Issue , 2011, Pages 3313-3318

  Qian, Ting ^a   Jaeger, T Florian ^a  

^a UNIVERSITY OF ROCHESTER   (United States)

Author keywords

communicative efficiency; discourse production; topic shift

Indexed keywords

EFFICIENCY;

COMMUNICATIVE EFFICIENCY; CONDITION; DISCOURSE PRODUCTION; LATENT TOPIC MODEL; TOPIC SHIFT; TOPIC STRUCTURES;

ENTROPY;

EID: 85139461600     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Andrzejewski, D., Mulhern, A., Liblit, B., & Zhu, X. (2007). Statistical debugging using latent topic models. In ECML (p. 6-17).
2. Aylett, M. P., & Turk, A. (2004). The smooth signal redundancy hypothesis: A functional explanation for relationships between redundancy, prosodic prominence, and duration in spontaneous speech. Lang Speech, 47(1), 31-56.
3. Bird, S., Loper, E., & Klein, E. (2009). Natural language processing with python. O’Reilly.
4. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. J Mach Learn Res, 3, 993-1022.
5. Brown, P. M., & Dell, G. S. (1987). Adapting production to comprehension: The explicit mention of instruments. Cognitive Psychology, 19, 441-472.
6. Clark, H. H., & Marshal, C. R. (1981). Elements of discourse understanding. In A. K. Joshi, B. L. Webber, & I. A. Sag (Eds.), (chap. Definite reference and mutual knowledge). Cambridge University Press.
7. Ferrer i Cancho, R., & Díaz-Guilera, A. (2007). The global minima of the communicative energy of natural communication systems. J Stat Mech-Theory E. (P06009)
8. Genzel, D., & Charniak, E. (2002). Entropy rate constancy in text. In ACL (pp. 199–206).
9. Genzel, D., & Charniak, E. (2003). Variation of entropy and parse trees of sentences as a function of the sentence number. In EMNLP (p. 65-72).
10. Griffiths, T., & Steyvers, M. (2004). Finding scientific topics. PNAS, 5228-5235.
11. Jaeger, T. F. (2010). Redundancy and reduction: Speakers manage syntactic information density. Cognitive Psychol, 61, 23-62.
12. Keller, F. (2004). The entropy rate principle as a predictor of processing effort: An evaluation against eye-tracking data. In EMNLP (p. 317-324).
13. Levy, R., & Jaeger, T. F. (2007). Speakers optimize information density through syntactic reduction. In NIPS (p. 849-856).
14. Piantadosi, S., & Gibson, E. (2008). Uniform information density in discourse: a cross-corpus analysis of syntactic and lexical predictability. In CUNY.
15. Pinheiro, J. C., & Bates, D. M. (2000). Mixed-effects models in S and S-PLUS. New York, NY: Springer-Verlag.
16. Qian, T., & Jaeger, T. F. (2009). Evidence for efficient language production in chinese. In CogSci09 (p. 851-856).
17. Qian, T., & Jaeger, T. F. (2010). Close = relevant? the role of context in efficient language production. In ACL 2010, CMCL Workshop.
18. Qian, T., & Jaeger, T. F. (submitted). Cue weight decay in communicatively efficient discourse production.
19. Shannon, C. E. (1948). A mathematical theory of communications. Bell Labs Tech J, 27(4), 623-656.