Combining feature norms and text data with topic models

Acta Psychologica

Volumn 133, Issue 3, 2010, Pages 234-243

  Steyvers, Mark ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Background knowledge; Bayesian models; Feature norms; Feature representations; Semantic cognition; Semantic spaces; Topic models

Indexed keywords

ARTICLE; COGNITION; DECISION MAKING; HUMAN; LEARNING; PSYCHOLOGICAL MODEL; SEMANTICS; STATISTICAL MODEL;

COGNITION; HUMANS; JUDGMENT; LEARNING; MODELS, PSYCHOLOGICAL; MODELS, STATISTICAL; SEMANTICS;

EID: 77649189749     PISSN: 00016918     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.actpsy.2009.10.010     Document Type: Article

References (47)

1. Andrews, M., Vigliocco, G., & Vinson, D. (2005). The role of attributional and distributional information in semantic representation In B. Bara, L. Barsalou, & M. Bucciarelli (Eds.), Proceedings of the twenty seventh annual conference of the cognitive science society.
2. Baroni, M., & Lenci, A. (2008). Concepts and properties in word spaces. In Alessandro Lenci (Ed.), From context to meaning: Distributional models of the lexicon in linguistics and cognitive science (special issue of the Italian journal of Linguistics, 20(1), 55-88).
3. Barselou Lawrence.W. Ad hoc categories. Memory and Cognition 1983, 11:211-227.
4. Battig W.F., Montague W.E. Category norms for verbal items in 56 categories: A replication and extension of the connecticut category norms. Journal of Experimental Psychology Monographs 1969, 80:1-45.
5. Blei, D., & Lafferty, J. (2006). Correlated topic models. In Y. Weiss, B. Schölkopf, & J. Platt (Eds.), Advances in neural information processing systems (Vol. 18, pp. 147-154). Cambridge, MA: MIT Press.
6. Blei D.M., Griffiths T.L., Jordan M.I., Tenenbaum J.B. Hierarchical topic models and the nested Chinese restaurant process. Advances in neural information processing systems 2004, Vol. 16. MIT Press, Cambridge, MA.
7. Blei D.M., Ng A.Y., Jordan M.I. Latent Dirichlet allocation. Journal of Machine Learning Research 2003, 3:993-1022.
8. Boyd-Graber, J., & Blei, D. (2008). Syntactic topic models. In Neural information processing systems. (Vol. 21, pp. 185-192). Cambridge, MA: MIT Press.
9. Buntine W.L., Jakulin A. Applying discrete PCA in data analysis. Proceedings of the 20th conference on uncertainty in artificial intelligence 2004, 59-66. Morgan Kaufmann Publishers, San Francisco, CA. M. Chickering, J. Halpern (Eds.).
10. Chemudugunta, C., Holloway, A., Smyth, P., & Steyvers, M. (2008). Modeling documents by combining semantic concepts with unsupervised statistical learning. In Proceedings of the 7th international semantic web conference (pp. 229-244). Berlin: Springer Verlag.
11. Chemudugunta, C., Smyth, P., & Steyvers, M. (2008a). Combining concept hierarchies and statistical topic models. In ACM 17th conference on information and knowledge management.
12. Chemudugunta, C., Smyth, P., & Steyvers, M. (2008b). Text modeling using unsupervised topic models and concept hierarchies. Technical report, url: http://arxiv.org/abs/0808.0973.
13. Collins A.M., Quillian M.R. Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Memory 1969, 8(2):240-248.
14. Cutting, D. R., Karger, D., Pedersen, J. O., & Tukey, J. W. (1992). Scatter/gather: A cluster-based approach to browsing large document collections. In Proceedings of the fifteenth annual international ACM SIGIR conference on research and development in information retrieval (pp. 318-329). New York, NY: ACM Press.
15. De Deyne S., Verheyen S., Ameel E., Vanpaemel W., Dry M., Voorspoels W., et al. Exemplar by feature applicability matrices and other Dutch normative data for semantic concepts. Behavior Research Methods 2008, 40(4):1030-1048.
16. Griffiths T.L., Kemp C., Tenenbaum J.B. Bayesian models of cognition. Cambridge handbook of computational cognitive modeling 2008, 59-100. Cambridge University Press, Cambridge, MA. R. Sun (Ed.).
17. Griffiths T.L., Steyvers M. Finding scientific topics. Proceedings of the National Academy of Science 2004, 101:5228-5235.
18. Griffiths T.L., Steyvers M., Blei D.M., Tenenbaum J.B. Integrating topics and syntax. Advances in neural information processing 2005, Vol. 17. MIT Press, Cambridge, MA.
19. Griffiths T.L., Steyvers M., Tenenbaum J.B.T. Topics in semantic representation. Psychological Review 2007, 114(2):211-244.
20. Havasi, C. H., Speer, R., & Alonso, J. (2007). ConceptNet 3: a flexible, multilingual semantic network for common sense knowledge. In Proceedings of recent advances in natural language processing.
21. Hofmann, T. (1999). Probabilistic latent semantic indexing. In Proceedings of the 22nd annual ACM conference on research and development in information retrieval (pp. 50-57). New York, NY: ACM Press.
22. Kohonen T., Kaski S., Lagus K., Salojärvi J., Honkela J., Paatero V., et al. Self organization of a massive document collection. IEEE Transactions on Neural Networks (Special Issue on Neural Networks for Data Mining and Knowledge Discovery) 2000, 11-3:574-585.
23. Landauer T.K., Dumais S.T. A solution to Plato's problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review 1997, 104:211-240.
24. Lee M.D. Three case studies in the Bayesian analysis of cognitive models. Psychonomic Bulletin and Review 2008, 15(1):1-15.
25. Li, W., Blei, D., & McCallum, A. (2007). Nonparametric Bayes Pachinko allocation. In Conference on uncertainty in artificial intelligence (UAI).
26. McRae K., Cree G.S., Seidenberg M.S., McNorgan C. Semantic feature production norms for a large set of living and nonliving things. Behavior Research Methods 2005, 37:547-559.
27. McRae K., de Sa V.R., Seidenberg M.S. On the nature and scope of featural representations of word meaning. Journal of Experimental Psychology 1997, 126(2):99-130.
28. Minka, T. P. (2000). Estimating a Dirichlet distribution. Technical report. Massachusetts Institute of Technology.
29. Navarro D.J., Perfors A.F. Similarity, feature discovery and the size principle. Acta Psychologica 2010, 133(3):256-268.
30. Newman, D., Chemudugunta, C., Smyth, P., & Steyvers, M. (2006). Analyzing entities and topics in news articles using statistical topic models. In Springer lecture notes in computer science (LNCS) series - IEEE international conference on intelligence and security informatics.
31. Popescul, A., Ungar, L. H., Flake, G. W., Lawrence, S., & Giles, C. L. (2000). Clustering and identifying temporal trends in document databases. In Proceedings of the IEEE advances in digital libraries (pp. 173-182). Los Alamitos, CA: IEEE Computer Society.
32. Rogers T., McClelland J. Semantic cognition: A parallel distributed processing approach 2004, The MIT Press, Cambridge.
33. Rosen-Zvi, M., Chemudugunta, C., Griffiths, T., Smyth, P., & Steyvers, M. (2010). Learning author-topic models from text corpora. ACM Transactions on Information Systems, 28(1).
34. Ruts W., De Deyne S., Ameel E., Vanpaemel W., Verbeemen T., Storms G. Flemish norm data for 13 natural concepts and 343 exemplars. Behavior Research Methods, Instruments, and Computers 2004, 36:506-515.
35. Smith E.E., Shoben E.J., Rips L.J. Structure and process in semantic memory: A featural model for semantic decisions. Psychological Review 1974, 81(3):214-241.
36. Steyvers, M., Chemudugunta, C., & Smyth, P. (submitted for publication). Combining background knowledge and learned topics. Topics in Cognitive Science (topiCS).
37. Steyvers M., Griffiths T.L. Probabilistic topic models. Handbook of latent semantic analysis 2007, Erlbaum. T. Landauer, D. McNamara, S. Dennis, W. Kintsch (Eds.).
38. Storms G. Flemish category norms for exemplars of 39 exemplars: A replication of the Battig and Montague (1969) category norms. Psychologica Belgica 2001, 41:145-168.
39. Teh Y.W., Jordan M., Beal M., Blei D. Hierarchical Dirichlet processes. Journal of the American Statistical Association 2006, 101(476):1566-1581.
40. Tenenbaum J.B., Griffiths T.L. Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences 2001, 24:629-641.
41. Vandekerckhove J., Verheyen S., Tuerlinckx F. A crossed random effects diffusion model for speeded semantic categorization decisions. Acta Psychologica 2010, 133(3):269-282.
42. Vigliocco G., Vinson D.P., Lewis W., Garrett M.F. Representing the meanings of object and action words: The featural and unitary semantic space hypothesis. Cognitive Psychology 2004, 48:422-488.
43. Vinson D., Vigliocco G. Semantic feature production norms for a large set of objects and events. Behavior Research Methods 2008, 40(1):183-190.
44. Wallach, H. (2006). Topic modeling: Beyond bag-of-words. In Proceedings of the 23rd international conference on machine learning (pp. 977-984). Pittsburgh, Pennsylvania, US.
45. Wang, C., Blei, D., & Heckerman, D. (2008). Continuous time dynamic topic models. In Uncertainty in artificial intelligence (UAI).
46. Zeigenfuse, M. D., & Lee, M. D. (this issue). Psychological contaminants as missing data: A latent assignment approach. Acta Psychologica.
47. Zeigenfuse M.D., Lee M.D. Finding feature representations of stimuli: Combining feature generation and similarity judgment tasks. Proceedings of the 30th annual conference of the cognitive science society 2008, 1825-1830. Cognitive Science Society, Austin, TX. V. Sloutsky, B. Love, K. McRae (Eds.).