Have a personal or library account? Click to login
Predicting Lexical Norms: A Comparison between a Word Association Model and Text-Based Word Co-occurrence Models Cover

Predicting Lexical Norms: A Comparison between a Word Association Model and Text-Based Word Co-occurrence Models

Journal of Cognition
Volume 1 (2017): Issue 1
By: Hendrik Vankrunkelsven,  Steven Verheyen,  Gert Storms and  Simon De Deyne  
Open Access
|Nov 2018

References

  1. 1Aitchison, J. (2003). Words in the Mind: An Introduction to the Mental Lexicon. Hoboken, NJ: John Wiley & Sons.
    Back to article
  2. 2Bestgen, Y., & Vincze, N. (2012). Checking and bootstrapping lexical norms by means of word similarity indexes. Behavior Research Methods, 44, 998–1006. DOI: 10.3758/s13428-012-0195-z
    Back to article
  3. 3Binder, J. R., Westbury, C. F., McKiernan, K. A., Possing, E. T., & Medler, D. A. (2005). Distinct brain systems for processing concrete and abstract concepts. Journal of Cognitive Neuroscience, 17, 905–917. DOI: 10.1162/0898929054021102
    Back to article
  4. 4Borg, I., & Groenen, P. J. F. (2005). Modern multidimensional scaling: Theory and applications. New York, NY: Springer.
    Back to article
  5. 5Bradley, M. M., & Lang, P. J. (1999). Affective norms for English words (ANEW): Stimuli, instruction manual and affective ratings (Tech. Rep. No. C-1). Gainesville, FL: Center for Research in Psychophysiology, University of Florida.
    Back to article
  6. 6Bradley, M. M., & Lang, P. J. (2017). Affective Norms for English Words (ANEW): Instruction manual and affective ratings. (Technical Report C-3). Gainesville, FL: UF Center for the Study of Emotion and Attention.
    Back to article
  7. 7Brants, T., & Franz, A. (2006). Web 1 T 5-gram Version 1. Philadelphia, PA: Linguistic Data Consortium.
    Back to article
  8. 8Brysbaert, M., & Ghyselinck, M. (2006). The effect of age of acquisition: Partly frequency related, partly frequency independent. Visual Cognition, 13, 992–1011. DOI: 10.1080/13506280544000165
    Back to article
  9. 9Brysbaert, M., Stevens, M., De Deyne, S., Voorspoels, W., & Storms, G. (2014). Norms of age of acquisition and concreteness for 30,000 Dutch words. Acta Psychologica, 150, 80–84. DOI: 10.1016/j.actpsy.2014.04.010
    Back to article
  10. 10Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46, 904–911. DOI: 10.3758/s13428-013-0403-5
    Back to article
  11. 11Bullinaria, J. A., & Levy, J. P. (2007). Extracting semantic representations from word co-occurrence statistics: A computational study. Behavior Research Methods, 39, 510–526. DOI: 10.3758/BF03193020
    Back to article
  12. 12Bullinaria, J. A., & Levy, J. P. (2012). Extracting semantic representations from word co-occurrence statistics: Stop-lists, stemming, and SVD. Behavior Research Methods, 44, 890–907. DOI: 10.3758/s13428-011-0183-8
    Back to article
  13. 13Coltheart, M. (1981). The MRC psycholinguistic database. The Quarterly Journal of Experimental Psychology, 33, 497–505. DOI: 10.1080/14640748108400805
    Back to article
  14. 14Cramer, P. (1968). Word association. New York, NY: Academic Press.
    Back to article
  15. 15De Deyne, S., Navarro, D. J., Collell, G., & Perfors, A. (2018). Visual and affective grounding in language and mind. Manuscript under review.
    Back to article
  16. 16De Deyne, S., Navarro, D. J., Perfors, A., & Storms, G. (2016). Structure at every scale: A semantic network account of the similarities between unrelated concepts. Journal of Experimental Psychology. General, 145, 1228–1254. DOI: 10.1037/xge0000192
    Back to article
  17. 17De Deyne, S., Navarro, D. J., Perfors, A., Brysbaert, M., & Storms, G. (2018). The “Small World of Words” English word association norms for over 12,000 cue words. Behavior Research Methods. DOI: 10.3758/s13428-018-1115-7
    Back to article
  18. 18De Deyne, S., Navarro, D. J., & Storms, G. (2013). Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations. Behavior Research Methods, 45, 480–498. DOI: 10.3758/s13428-012-0260-7
    Back to article
  19. 19De Deyne, S., Perfors, A., & Navarro, D. J. (2017). Predicting human similarity judgments with distributional models: The value of word associations. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, 4806–4810. DOI: 10.24963/ijcai.2017/671
    Back to article
  20. 20De Deyne, S., Verheyen, S., & Storms, G. (2015). The role of corpus size and syntax in deriving lexico-semantic representations for a wide range of concepts. Quarterly Journal of Experimental Psychology, 68, 1643–1664. DOI: 10.1080/17470218.2014.994098
    Back to article
  21. 21De Deyne, S., Voorspoels, W., Verheyen, S., Navarro, D. J., & Storms, G. (2014). Accounting for graded structure in adjective categories with valence-based opposition relationships. Language, Cognition and Neuroscience, 29, 568–583. DOI: 10.1080/01690965.2013.794294
    Back to article
  22. 22Deese, J. (1965). The Structure of Associations in Language and Thought. Baltimore, MD: Johns Hopkins University Press.
    Back to article
  23. 23de Groot, A. M. B. (1995). Determinants of bilingual lexicosemantic organization. Computer Assisted Language Learning, 8, 151–180. DOI: 10.1080/0958822940080204
    Back to article
  24. 24De Houwer, J., Crombez, G., Baeyens, F., & Hermans, D. (2001). On the generality of the affective Simon effect. Cognition and Emotion, 15, 189–206. DOI: 10.1080/02699930125883
    Back to article
  25. 25Diedenhofen, B., & Musch, J. (2015). cocor: A Comprehensive Solution for the Statistical Comparison of Correlations. PLoS ONE, 10, e0121945. DOI: 10.1371/journal.pone.0121945
    Back to article
  26. 26Fazio, R. H. (2001). On the automatic activation of associated evaluations: An overview. Cognition and Emotion, 15, 115–141. DOI: 10.1080/02699930125908
    Back to article
  27. 27Firth, J. R. (1968). Selected Papers of J. R. Firth, 1952–59. Indiana University Press.
    Back to article
  28. 28Fisher, A. V. (2010). What’s in the name? Or how rocks and stones are different from bunnies and rabbits. Journal of Experimental Child Psychology, 105, 198–212. DOI: 10.1016/j.jecp.2009.11.001
    Back to article
  29. 29Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh, Scotland: Oliver and Boyd.
    Back to article
  30. 30Hampton, J. A. (1979). Polymorphous concepts in semantic memory. Journal of Verbal Learning and Verbal Behavior, 18, 441–461. DOI: 10.1016/S0022-5371(79)90246-9
    Back to article
  31. 31Heylen, K., Peirsman, Y., & Geeraerts, D. (2008). Automatic synonymy extraction. In: Verberne, S., van Halteren, H., & Coppen, P.-A. (eds.), A comparison of syntactic context models: LOT computational linguistics in the Netherlands 2007, 101–116. Utrecht: Netherlands National Graduate School of Linguistics.
    Back to article
  32. 32Hofmann, M. J., Kuchinke, L., Biemann, C., Tamm, S., & Jacobs, A. M. (2011). Remembering words in context as predicted by an associative read-out model. Frontiers in Psychology, 2, 252. DOI: 10.3389/fpsyg.2011.00252
    Back to article
  33. 33Hollis, G., & Westbury, C. (2016). The principals of meaning: Extracting semantic dimensions from co-occurrence models of semantics. Psychonomic Bulletin & Review, 23, 1744–1756. DOI: 10.3758/s13423-016-1053-2
    Back to article
  34. 34Isen, A. M., Johnson, M. M., Mertz, E., & Robinson, G. F. (1985). The influence of positive affect on the unusualness of word associations. Journal of Personality and Social Psychology, 48, 1413–1426. DOI: 10.1037/0022-3514.48.6.1413
    Back to article
  35. 35Jones, M. N., Hills, T. T., & Todd, P. M. (2015). Hidden processes in structural representations: A reply to Abbott, Austerweil, and Griffiths (2015). Psychological Review, 122, 570–574. DOI: 10.1037/a0039248
    Back to article
  36. 36Klauer, K. C. (1997). Affective priming. European Review of Social Psychology, 8, 67–103. DOI: 10.1080/14792779643000083
    Back to article
  37. 37Kousta, S.-T., Vigliocco, G., Vinson, D. P., Andrews, M., & Del Campo, E. (2011). The representation of abstract words: Why emotion matters. Journal of Experimental Psychology. General, 140, 14–34. DOI: 10.1037/a0021446
    Back to article
  38. 38Kruskal, J. B., & Wish, M. (1978). Multidimensional Scaling. Beverly Hills, CA: Sage. DOI: 10.4135/9781412985130
    Back to article
  39. 39Kuperman, V., Estes, Z., Brysbaert, M., & Warriner, A. B. (2014). Emotion and language: Valence and arousal affect word recognition. Journal of Experimental Psychology. General, 143, 1065–1081. DOI: 10.1037/a0035669
    Back to article
  40. 40Kuperman, V., Stadthagen-Gonzalez, H., & Brysbaert, M. (2012). Age-of-acquisition ratings for 30,000 English words. Behavior Research Methods, 44, 978–990. DOI: 10.3758/s13428-012-0210-4
    Back to article
  41. 41Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104, 211–240. DOI: 10.1037/0033-295X.104.2.211
    Back to article
  42. 42Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse Processes, 25, 259–284. DOI: 10.1080/01638539809545028
    Back to article
  43. 43Lane, R. D., Chua, P. M., & Dolan, R. J. (1999). Common effects of emotional valence, arousal and attention on neural activation during visual processing of pictures. Neuropsychologia, 37, 989–997. DOI: 10.1016/S0028-3932(99)00017-2
    Back to article
  44. 44Lang, P. J., Bradley, M. M., Fitzsimmons, J. R., Cuthbert, B. N., Scott, J. D., Moulder, B., & Nangia, V. (1998). Emotional arousal and activation of the visual cortex: An fMRI analysis. Psychophysiology, 35, 199–210. DOI: 10.1111/1469-8986.3520199
    Back to article
  45. 45Levy, O., & Goldberg, Y. (2014). Neural Word Embedding As Implicit Matrix Factorization. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, 2, 2177–2185. Cambridge, MA: MIT Press.
    Back to article
  46. 46Maddock, R. J., Garrett, A. S., & Buonocore, M. H. (2003). Posterior cingulate cortex activation by emotional words: fMRI evidence from a valence decision task. Human Brain Mapping, 18, 30–41. DOI: 10.1002/hbm.10075
    Back to article
  47. 47Mandera, P., Keuleers, E., & Brysbaert, M. (2015). How useful are corpus-based methods for extrapolating psycholinguistic variables? Quarterly Journal of Experimental Psychology, 68, 1623–1642. DOI: 10.1080/17470218.2014.988735
    Back to article
  48. 48Mervis, C. B., & Rosch, E. (1981). Categorization of natural objects. Annual Review of Psychology, 32, 89–115. DOI: 10.1146/annurev.ps.32.020181.000513
    Back to article
  49. 49Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. https://arxiv.org/abs/1301.3781/.
    Back to article
  50. 50Moffat, M., Siakaluk, P. D., Sidhu, D. M., & Pexman, P. M. (2015). Situated conceptualization and semantic processing: Effects of emotional experience and context availability in semantic categorization and naming tasks. Psychonomic Bulletin & Review, 22, 408–419. DOI: 10.3758/s13423-014-0696-0
    Back to article
  51. 51Moors, A., De Houwer, J., Hermans, D., Wanmaker, S., van Schie, K., Van Harmelen, A.-L., De Schryver, M., De Winne, J., & Brysbaert, M. (2013). Norms of valence, arousal, dominance, and age of acquisition for 4,300 Dutch words. Behavior Research Methods, 45, 169–177. DOI: 10.3758/s13428-012-0243-8
    Back to article
  52. 52Mourão-Miranda, J., Volchan, E., Moll, J., de Oliveira-Souza, R., Oliveira, L., Bramati, I., Pessoa, L., et al. (2003). Contributions of stimulus valence and arousal to visual activation during emotional perception. NeuroImage, 20, 1955–1963. DOI: 10.1016/j.neuroimage.2003.08.011
    Back to article
  53. 53Murphy, G. (2002). The Big Book of Concepts. Cambridge, MA: MIT Press.
    Back to article
  54. 54Murphy, G. L., & Lassaline, M. E. (1997). Hierarchical structure in concepts and the basic level of categorization. In: Lamberts, K., & Shanks, D. R. (eds.), Knowledge, concepts and categories, 93–131. Cambridge, MA: MIT Press.
    Back to article
  55. 55Nematzadeh, A., Meylan, S., & Griffiths, T. (2017). Evaluating vector-space models of word representation, or, the unreasonable effectiveness of counting words near other words. In: Gunzelmann, G., Howes, A., Tenbrink, T., & Davelaar, E. (eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society, 859–864. Austin, TX: Cognitive Science Society.
    Back to article
  56. 56Niedenthal, P. M., Halberstadt, J. B., & Innes-Ker, Å. H. (1999). Emotional response categorization. Psychological Review, 106, 337–361. DOI: 10.1037/0033-295X.106.2.337
    Back to article
  57. 57Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The Measurement of Meaning. Urbana, IL: University of Illinois Press.
    Back to article
  58. 58Osherson, D. N., Smith, E. E., Wilkie, O., López, A., & Shafir, E. (1990). Category-based induction. Psychological Review, 97, 185–200. DOI: 10.1037/0033-295X.97.2.185
    Back to article
  59. 59Padó, S., & Lapata, M. (2007). Dependency-based construction of semantic space models. Comput. Linguist., 33, 161–199. DOI: 10.1162/coli.2007.33.2.161
    Back to article
  60. 60Paivio, A., Walsh, M., & Bons, T. (1994). Concreteness effects on memory: When and why? Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 1196–1204. DOI: 10.1037/0278-7393.20.5.1196
    Back to article
  61. 61Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. (1996). Understanding normal and impaired word reading: Computational principles in quasi-regular domains. Psychological Review, 103, 56–115. DOI: 10.1037/0033-295X.103.1.56
    Back to article
  62. 62Recchia, G., & Louwerse, M. M. (2015). Reproducing affective norms with lexical co-occurrence statistics: Predicting valence, arousal, and dominance. The Quarterly Journal of Experimental Psychology, 68, 1584–1598. DOI: 10.1080/17470218.2014.941296
    Back to article
  63. 63Rosch, E., & Mervis, C. B. (1975). Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 7, 573–605. DOI: 10.1016/0010-0285(75)90024-9
    Back to article
  64. 64Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382–439. DOI: 10.1016/0010-0285(76)90013-X
    Back to article
  65. 65Schwanenflugel, P. J., Harnishfeger, K. K., & Stowe, R. W. (1988). Context availability and lexical decisions for abstract and concrete words. Journal of Memory and Language, 27, 499–520. DOI: 10.1016/0749-596X(88)90022-8
    Back to article
  66. 66Smith, E. E., Shafir, E., & Osherson, D. (1993). Similarity, plausibility, and judgments of probability. Cognition, 49, 67–96. DOI: 10.1016/0010-0277(93)90036-U
    Back to article
  67. 67Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15, 72–101. DOI: 10.2307/1412159
    Back to article
  68. 68Steyvers, M., & Tenenbaum, J. B. (2005). The large-scale structure of semantic networks: Statistical analyses and a model of semantic growth. Cognitive Science, 29, 41–78. DOI: 10.1207/s15516709cog2901_3
    Back to article
  69. 69Szalay, L. B., & Deese, J. (1978). Subjective meaning and culture: An assessment through word associations. Hillsdale, NJ: Lawrence Erlbaum.
    Back to article
  70. 70Van Rensbergen, B., Storms, G., & De Deyne, S. (2015). Examining assortativity in the mental lexicon: Evidence from word associations. Psychonomic Bulletin & Review, 22, 1717–1724. DOI: 10.3758/s13423-015-0832-5
    Back to article
  71. 71Vankrunkelsven, H., Verheyen, S., De Deyne, S., & Storms, G. (2015). Predicting lexical norms using a word association corpus. In: Noelle, D., Dale, R., Warlaumont, A., Yoshimi, J., Matlock, T., Jennings, C., & Maglio, P. (eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society, 2463–2468. Austin, TX: Cognitive Science Society.
    Back to article
  72. 72Warriner, A. B., Kuperman, V., & Brysbaert, M. (2013). Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods, 45(4), 1191–1207. DOI: 10.3758/s13428-012-0314-x
    Back to article
  73. 73Wittgenstein, L. (1953). Philosophical Investigations. New York, NY: Macmillan.
    Back to article
  74. 74Zevin, J. D., & Seidenberg, M. S. (2002). Age of acquisition effects in word reading and other tasks. Journal of Memory and Language, 47, 1–29. DOI: 10.1006/jmla.2001.2834
    Back to article
DOI: https://doi.org/10.5334/joc.50 | Journal eISSN: 2514-4820
Journal RSS Feed
Language: English
Submitted on: Jul 1, 2018
Accepted on: Nov 6, 2018
Published on: Nov 27, 2018
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
word associations,
k-nearest neighbors,
lexical norms,
affective word characteristics,
concreteness,
age of acquisition

© 2018 Hendrik Vankrunkelsven, Steven Verheyen, Gert Storms, Simon De Deyne, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Previous articleVolume 1 (2017): Issue 1Next article