A corpus-based evaluation method for distributional semantic models

Proceedings of the Annual Meeting of the Association for Computational Linguistics

Volumn , Issue , 2013, Pages 165-171

  Fourtassi, Abdellah ^a,b   Dupoux, Emmanuel ^b,c  

^a ECOLE NORMALE SUPÉRIEURE   (France)

^b CNRS   (France)

^c ÉCOLE DES HAUTES ÉTUDES EN SCIENCES SOCIALES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CORPUS-BASED; CORPUS-BASED MEASURES; DETECTION TASKS; DISTRIBUTIONAL SEMANTICS; EVALUATION METHODS; GOLD STANDARDS; LEXICAL SEMANTICS; SEMANTIC MODELLING; SEMANTIC SIMILARITY; SIMILARITY SPACES;

EID: 85072749117     PISSN: 0736587X     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Andrews, M., G. Vigliocco, and D. Vinson (2009). Integrating experiential and distributional data to learn semantic representations. Psychological Review 116, 463–498.
2. Baroni, M. and A. Lenci (2011). How we BLESSed distributional semantic evaluation. In Proceedings of the EMNLP 2011 Geometrical Models for Natural Language Semantics (GEMS 2011) Workshop, East Stroudsburg PA: ACL, pp. 1–10.
3. Blei, D. (2012). Probabilistic topic models. Communications of the ACM 55(4), 77–84.
4. Dagan, I., L. Lee, and F. Pereira (1997). Similarity-based methods for word sense disambiguation. In Proceedings of the 35th ACL/8th EACL, pp. 56–63.
5. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874.
6. Gale, W., K. Church, and D. Yarowsky (1992). Work on statistical methods for word sense disambiguation. Workings notes, AAAI Fall Symposium Series, Probabilistic Approaches to Natural Language, 54–60.
7. Griffiths, T., M. Steyvers, and J. Tenenbaum (2007). Topics in semantic representation. Psychological Review 114, 114–244.
8. Harris, Z. (1954). Distributional structure. Word 10(23), 146–162.
9. Hutchison, K., D. Balota, M. Cortese, and J. Watson (2008). Predicting semantic priming at the item level. Quarterly Journal of Experimental Psychology 61(7), 1036–1066.
10. Kievit-Kylar, B. and M. N. Jones (2012). Visualizing multiple word similarity measures. Behavior Research Methods 44(3), 656–674.
11. Landauer, T. and S. Dumais (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.
12. Lewandowsky, S. and S. Farrell (2011). Computational modeling in cognition: principles and practice. Thousand Oaks, Calif.: Sage Publications.
13. Lindsey, R., V. Veksler, and A. G. andWayne Gray (2007). Be wary of what your computer reads: The effects of corpus selection on measuring semantic relatedness. In Proceedings of the Eighth International Conference on Cognitive Modeling, pp. 279–284.
14. Luhn, H. P. (1958). The automatic creation of literature abstracts. IBM Journal of Research and Development 2(2), 157–165.
15. Nelson, D., C. McEvoy, and T. Schreiber (1998). The university of south florida word association, rhyme, and word fragment norms.
16. Reese, S., G. Boleda, M. Cuadros, L. Padro, and G. Rigau (2010). Wikicorpus: A word-sense disambiguated multilingual wikipedia corpus. In Proceedings of 7th Language Resources and Evaluation Conference (LREC’10), La Valleta, Malta.
17. Řehůřek, R. and P. Sojka (2010). Software framework for topic modelling with large corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, Valletta, Malta, pp. 45–50.
18. Reshef, D., Y. Reshef, H. Finucane, S. Gross-man, G. McVean, P. Turnbaugh, E. Lander, M. Mitzenmacher, and P. Sabeti (2011). Detecting novel associations in large datasets. Science 334(6062), 1518–1524.
19. Sridharan, S. and B. Murphy (2012). Modeling word meaning: distributional semantics and the sorpus quality-quantity trade-off. In Proceedings of the 3rd Workshop on Cognitive Aspects of the Lexicon, COLING 2012, Mumbai, pp. 53–68.
20. Turney, P. D. and P. Pantel (2010). From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research 37, 141–188.