Selecting corpus-semantic models for neurolinguistic decoding

*SEM 2012 - 1st Joint Conference on Lexical and Computational Semantics

Volumn 1, Issue , 2012, Pages 114-123

  Murphy, Brian ^a   Talukdar, Partha ^a   Mitchell, Tom ^a  

^a Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

NEUROIMAGING; NEURONS;

CO-OCCURRENCE; COMPUTATIONAL COSTS; CORPUS SEMANTICS; DIRECTIONAL INFORMATION; INDIVIDUAL CONCEPTS; MANUAL INTERVENTION; NEURAL ACTIVITY; STATE-OF-THE-ART TECHNIQUES;

SEMANTICS;

EID: 85006096517     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (50)

1. Almuhareb, A. and Poesio, M. (2004). Attributebased and value-based clustering: An evaluation. In Proceedings of EMNLP, pages 158-165.
2. Baroni, M. and Lenci, A. (2010). Distributional Memory : A General Framework for Corpus-Based Semantics. Computational Linguistics, 36(4):673- 721.
3. Baroni, M., Murphy, B., Barbu, E., and Poesio, M. (2010). Strudel: A corpus-based semantic model based on properties and types. Cognitive Science, 34(2):222-254.
4. Battig, W. F. and Montague, W. E. (1969). Category Norms for Verbal Items in 56 Categories: A Replication and Extension of the Connecticut Category Norms. Journal of Experimental Psychology Monographs, 80(3):1-46.
5. Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(4-5):993-1022.
6. Bradford, R. B. (2008). An empirical study of required dimensionality for large-scale latent semantic indexing applications. Proceeding of the 17th ACM conference on Information and knowledge mining CIKM 08, pages 153-162.
7. Brants, T. and Franz, A. (2006). Web 1T 5-gram Version 1.
8. Bullinaria, J. A. and Levy, J. P. (2007). Extracting semantic representations from word co-occurrence statistics: A computational study. Behavior Research Methods, 39(3):510-526.
9. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Jr., E. R. H., and Mitchell, T. M. (2010). Toward an Architecture for Never-Ending Language Learning. Artificial Intelligence, 2(4):3-3.
10. Chang, K.-m. K., Mitchell, T., and Just, M. A. (2011). Quantitative modeling of the neural representation of objects: how semantic feature norms can account for fMRI activation. NeuroImage, 56(2):716-727.
11. Curran, J. R. and Moens, M. (2002). Improvements in automatic thesaurus extraction. In SIGLEX, pages 59-66.
12. Deerwester, S., Dumais, S., Landauer, T., Furnas, G., and Harshman, R. (1990). Indexing by Latent Semantic Analysis. Journal of the American Society of Information Science, 41(6):391 - 407.
13. Devereux, B. and Kelly, C. (2010). Using fMRI activation to conceptual stimuli to evaluate methods for extracting conceptual representations from corpora. In Murphy, B., Korhonen, A., and Chang, K. K.-M., editors, 1st Workshop on Computational Neurolinguistics.
14. Friston, K. J., Ashburner, J. T., Kiebel, S. J., Nichols, T. E., and Penny, W. D. (2007). Statistical Parametric Mapping: The Analysis of Functional Brain Images, volume 8. Academic Press.
15. Grefenstette, G. (1994). Explorations in Automatic Thesaurus Discovery. Kluwer, Dordrecht.
16. Griffiths, T. L., Steyvers, M., and Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114(2):211-244.
17. Hall, J., Nilsson, J., Nivre, J., Eryigit, G., Megyesi, B., Nilsson, M., and Saers, M. (2007). Single Malt or Blended? A Study in Multilingual Parser Optimization. CoNLL Shared Task Session, pages 933-939.
18. Hastie, T., Tibshirani, R., and Friedman, J. (2011). The Elements of Statistical Learning, volume 18 of Springer Series in Statistics. Springer, 5th edition.
19. Jelodar, A. B., Alizadeh, M., and Khadivi, S. (2010). WordNet Based Features for Predicting Brain Activity associated with meanings of nouns. In Murphy, B., Korhonen, A., and Chang, K. K.-M., editors, 1st Workshop on Computational Neurolinguistics, pages 18-26.
20. Jones, E., Oliphant, T., Peterson, P., and Et Al. (2001). SciPy: Open source scientific tools for Python.
21. Kanejiya, D., Kumar, A., and Prasad, S. (2003). Automatic evaluation of students' answers using syntactically enhanced LSA. Building educational applications, NAACL, 2:53-60.
22. Kiss, G. R., Armstrong, C., Milroy, R., and Piper, J. (1973). An associative thesaurus of English and its computer analysis. In Aitken, A. J., Bailey, R. W., and Hamilton-Smith, N., editors, The Computer and Literary Studies. Edinburgh University Press.
23. Landauer, T. and Dumais, S. (1997). A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104(2):211-240.
24. Lee, D. D. and Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788-91.
25. Lehoucq, R. B., Sorensen, D. C., and Yang, C. (1998). Arpack users' guide: Solution of large scale eigenvalue problems with implicitly restarted Arnoldi methods. SIAM.
26. Lin, D. (1998). Automatic Retrieval and Clustering of Similar Words. In COLING-ACL, pages 768- 774.
27. Lin, D. and Pantel, P. (2001). DIRT - discovery of inference rules from text. Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining KDD 01, datamining:323-328.
28. Loper, E. and Bird, S. (2002). {NLTK}: The natural language toolkit. In ACL Workshop, volume 1, pages 63-70. Association for Computational Linguistics.
29. Lund, K. and Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical cooccurrence. Behavior Research Methods, Instruments, and Computers, 28:203-208.
30. Lund, K., Burgess, C., and Atchley, R. (1995). Semantic and associative priming in high dimensional semantic space. In Proceedings of the 17th Cognitive Science Society Meeting, pages 660-665.
31. Mitchell, T. M., Shinkareva, S. V., Carlson, A., Chang, K.-M., Malave, V. L., Mason, R. A., and Just, M. A. (2008). Predicting Human Brain Activity Associated with the Meanings of Nouns. Science, 320:1191-1195.
32. Murphy, B., Baroni, M., and Poesio, M. (2009). EEG responds to conceptual stimuli and corpus semantics. In Proceedings of EMNLP, pages 619-627. ACL.
33. Murphy, B., Korhonen, A., and Chang, K. K.- M., editors (2010). Proceedings of the 1st Workshop on Computational Neurolinguistics, NAACLHLT, Los Angeles. ACL.
34. Murphy, B., Talukdar, P., and Mitchell, T. (2012). Comparing Abstract and Concrete Conceptual Representations using Neurosemantic Decoding. In NAACL Workshop on Cognitive Modelling and Computational Linguistics.
35. Nancy Ide and Keith Suderman (2006). The American National Corpus First Release. Proceedings of the Fifth Language Resources and Evaluation Conference (LREC).
36. Nation, P. and Waring, R. (1997). Vocabulary size, text coverage and word lists. In Schmitt, N. and McCarthy, M., editors, Vocabulary Description acquisition and pedagogy, pages 6-19. Cambridge University Press.
37. Pado, S. and Lapata, M. (2007). Dependency-based construction of semantic space models. Computational Linguistics, 33(2):161-199.
38. Paice, C. D. (1990). Another stemmer. SIGIR Forum, 24(3):56-61.
39. Palatucci, M., Hinton, G., Pomerleau, D., and Mitchell, T. M. (2009). Zero-Shot Learning with Semantic Output Codes. Advances in Neural Information Processing Systems, 22:1-9.
40. Palatucci, M. M. (2011). Thought Recognition: Predicting and Decoding Brain Activity Using the Zero-Shot Learning Model. PhD thesis, Carnegie Mellon University.
41. Pereira, F., Detre, G., and Botvinick, M. (2011). Generating Text from Functional Brain Images. Frontiers in Human Neuroscience, 5:1-11.
42. Rapp, R. (2003). Word Sense Discovery Based on Sense Descriptor Dissimilarity. Proceedings of the Ninth Machine Translation Summit, pp:315-322.
43. Rehurek, R. and Sojka, P. (2010). Software Framework for Topic Modelling with Large Corpora. In New Challenges, LREC 2010, pages 45-50. ELRA.
44. Rubenstein, H. and Goodenough, J. B. (1965). Contextual correlates of synonymy. Communications of the ACM, 8(10):627-633.
45. Sahlgren, M. (2006). The Word-Space Model: Using distributional analysis to represent syntagmatic and paradigmatic relations between words in highdimensional vector spaces. Dissertation, Stockholm University.
46. Sandler, T., Talukdar, P. P., Ungar, L. H., and Blitzer, J. (2009). Regularized Learning with Networks of Features. Advances in Neural Information Processing Systems 21, 4:1401-1408.
47. Schutze, H. and Pedersen, J. (1993). A Vector Model for syntagmatic and paradigmatic relatedness. In Making Sense of Words Proceedings of the 9th Annual Conference of the University of Waterloo Centre for the New OED and Text Research, pages 104-113.
48. Shinkareva, S. V., Mason, R. A., Malave, V. L., Wang, W., Mitchell, T. M., and Just, M. A. (2008). Using fMRI Brain Activation to Identify Cognitive States Associated with Perception of Tools and Dwellings. PloS ONE, 3(1).
49. Turney, P. D. and Pantel, P. (2010). From Frequency to Meaning: Vector Space Models of Semantics. Artificial Intelligence, 37(1):141-188.
50. Widdows, D. (2003). Unsupervised methods for developing taxonomies by combining syntactic and statistical information. In NAACL, pages 197- 204. Association for Computational Linguistics.