1887
Volume 17, Issue 3
  • ISSN 1871-1340
  • E-ISSN: 1871-1375
Preview this article:

Available under the CC BY 4.0 license.
Loading

Article metrics loading...

/content/journals/10.1075/ml.00021.baa
2023-09-12
2025-02-14
Loading full text...

Full text loading...

/deliver/fulltext/ml.00021.baa.html?itemId=/content/journals/10.1075/ml.00021.baa&mimeType=html&fmt=ahah

References

  1. Amenta, S., Crepaldi, D., and Marelli, M.
    (2019) Consistency measures individuate dissociating semantic modulations in priming paradigms: A new look on semantics in the processing of (complex) words. Quarterly Journal of Experimental Psychology, 73(10), 1546–1563. 10.1177/1747021820927663
    https://doi.org/10.1177/1747021820927663 [Google Scholar]
  2. Baayen, R. H., Chuang, Y. -Y., Shafaei-Bajestan, E., and Blevins, J.
    (2019) The discriminative lexicon: A unified computational model for the lexicon and lexical processing in comprehension and production grounded not in (de)composition but in linear discriminative learning. Complexity. 10.1155/2019/4895891
    https://doi.org/10.1155/2019/4895891 [Google Scholar]
  3. Baayen, R. H. and Moscoso del Prado Martín, F.
    (2005) Semantic density and past-tense formation in three Germanic languages. Language, 811:666–698. 10.1353/lan.2005.0112
    https://doi.org/10.1353/lan.2005.0112 [Google Scholar]
  4. Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T.
    (2017) Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 51:135–146. 10.1162/tacl_a_00051
    https://doi.org/10.1162/tacl_a_00051 [Google Scholar]
  5. Bonami, O. and Paperno, D.
    (2018) Inflection vs. derivation in a distributional vector space. Lingue e Linguaggio, 17(2):173–195.
    [Google Scholar]
  6. Burgess, C. and Lund, K.
    (1998) The dynamics of meaning in memory. InDietrich, E. and Markman, A. B.editors, Cognitive dynamics: Conceptual change in humans and machines. Lawrence Erlbaum Associates.
    [Google Scholar]
  7. Chuang, Y., Vollmer, M. -L., Shafaei-Bajestan, E., Gahl, S., Hendrix, P., and Baayen, R. H.
    (2020) The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using linear discriminative learning. Behavior Research Methods. 531: 945–976. 10.3758/s13428‑020‑01356‑w
    https://doi.org/10.3758/s13428-020-01356-w [Google Scholar]
  8. Chuang, Y. Y., Kang, M., Luo, X. F., and Baayen, R. H.
    (2023) Vector space morphology with linear discriminative learning. InCrepaldi, D.editor, Linguistic morphology in the mind and brain. Routledge. 10.4324/9781003159759‑12
    https://doi.org/10.4324/9781003159759-12 [Google Scholar]
  9. Guzmán Naranjo, M.
    (2020) Analogy, complexity and predictability in the Russian nominal inflection system. Morphology, 30(3):219–262. 10.1007/s11525‑020‑09367‑1
    https://doi.org/10.1007/s11525-020-09367-1 [Google Scholar]
  10. Heitmeier, M. and Baayen, R. H.
    (2020) Simulating phonological and semantic impairment of English tense inflection with Linear Discriminative Learning. The Mental Lexicon, accepted. PsyArXiv. 15(3): 385–421. 10.1075/ml.20003.hei
    https://doi.org/10.1075/ml.20003.hei [Google Scholar]
  11. Kisselew, M., Pado, S., Palmer, A., and Šnajder, J.
    (2015) Obtaining a better understanding of distributional models of german derivational morphology. InProceedings of the 11th International Conference on Computational Semantics, pages58–63.
    [Google Scholar]
  12. 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. 10.1037/0033‑295X.104.2.211
    https://doi.org/10.1037/0033-295X.104.2.211 [Google Scholar]
  13. Maaten, L. v. d. and Hinton, G.
    (2008) Visualizing data using t-sne. Journal of machine learning research, 9(Nov): 2579–2605.
    [Google Scholar]
  14. Marelli, M., Amenta, S., and Crepaldi, D.
    (2014) Semantic transparency in free stems: the effect of orthography-semantics consistency in word recognition. Quarterly Journal of Experimental Psychology, 68(8): 1571–1583. 10.1080/17470218.2014.959709
    https://doi.org/10.1080/17470218.2014.959709 [Google Scholar]
  15. Marelli, M., Amenta, S., Morone, E. A., and Crepaldi, D.
    (2013) Meaning is in the beholder’s eye: Morpho-semantic effects in masked priming. Psychonomic bulletin & review, 20(3):534–541. 10.3758/s13423‑012‑0363‑2
    https://doi.org/10.3758/s13423-012-0363-2 [Google Scholar]
  16. Marelli, M. and Baroni, M.
    (2015) Affixation in semantic space: Modeling morpheme meanings with compositional distributional semantics. Psychological Review, 122(3):485–515. 10.1037/a0039267
    https://doi.org/10.1037/a0039267 [Google Scholar]
  17. Marelli, M., Gagné, C. L., and Spalding, T. L.
    (2017) Compounding as abstract operation in semantic space: Investigating relational effects through a large-scale, data-driven computational model. Cognition, 1661:207–224. 10.1016/j.cognition.2017.05.026
    https://doi.org/10.1016/j.cognition.2017.05.026 [Google Scholar]
  18. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J.
    (2013) Distributed representations of words and phrases and their compositionality. InAdvances in neural information processing systems, 261.
    [Google Scholar]
  19. Mitchell, J. and Lapata, M.
    (2008) Vector-based models of semantic composition. Inproceedings of ACL-08: HLT (pp.236–244).
    [Google Scholar]
  20. Nieder, J., Chuang, Y. Y., van de Vijver, R., & Baayen, H.
    (2023) A discriminative lexicon approach to word comprehension, production, and processing: Maltese plurals. Language, 99(2), 242–274. 10.1353/lan.2023.a900087
    https://doi.org/10.1353/lan.2023.a900087 [Google Scholar]
  21. Pennington, J., Socher, R., and Manning, C. D.
    (2014) Glove: Global vectors for word representation. InEmpirical Methods in Natural Language Processing (EMNLP), 1532–1543. 10.3115/v1/D14‑1162
    https://doi.org/10.3115/v1/D14-1162 [Google Scholar]
  22. Shaoul, C. and Westbury, C.
    (2010) Exploring lexical co-occurrence space using hidex. Behavior Research Methods, 42(2):393–413. 10.3758/BRM.42.2.393
    https://doi.org/10.3758/BRM.42.2.393 [Google Scholar]
  23. Shen, T. and Baayen, R. H.
    (2021) Adjective-noun compounds in Mandarin: a study on productivity. Corpus Linguistics and Linguistic Theory. 18(3), 543–572. 10.1515/cllt‑2020‑0059
    https://doi.org/10.1515/cllt-2020-0059 [Google Scholar]
  24. Westbury, C. and Hollis, G.
    (2018) Conceptualizing syntactic categories as semantic categories: Unifying part-of-speech identification and semantics using co-occurrence vector averaging. Behavioral Research Methods, 511: 1371–1398. 10.3758/s13428‑018‑1118‑4
    https://doi.org/10.3758/s13428-018-1118-4 [Google Scholar]
  25. Westbury, C., Keith, J., Briesemeister, B. B., Hofmann, M. J., and Jacobs, A. M.
    (2014) Avoid violence, rioting, and outrage; approach celebration, delight, and strength: Using large text corpora to compute valence, arousal, and the basic emotions. The Quarterly Journal of Experimental Psychology681: 1599–1622. 10.1080/17470218.2014.970204
    https://doi.org/10.1080/17470218.2014.970204 [Google Scholar]
  26. Westbury, C. and Wurm, L. H.
    (2022) Is it you you’re looking for? personal relevance as a principal component of semantics. The Mental Lexicon, 17(1):1–33. 10.1075/ml.20031.wes
    https://doi.org/10.1075/ml.20031.wes [Google Scholar]
  27. Williams, A., Blasi, D., Wolf-Sonkin, L., Wallach, H., and Cotterell, R.
    (2019) Quantifying the Semantic Core of Gender Systems. InProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages5734–5739, Hong Kong, China. Association for Computational Linguistics. 10.18653/v1/D19‑1577
    https://doi.org/10.18653/v1/D19-1577 [Google Scholar]
/content/journals/10.1075/ml.00021.baa
Loading
/content/journals/10.1075/ml.00021.baa
Loading

Data & Media loading...

  • Article Type: Introduction
This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error