Volume 6, Issue 1
  • ISSN 2542-3835
  • E-ISSN: 2542-3843
Buy:$35.00 + Taxes



Semantic embedding approaches commonly used in natural language processing such as transformer models have rarely been used to examine L2 lexical knowledge. Importantly, their performance has not been contrasted with more traditional annotation approaches to lexical knowledge. This study used NLP techniques related to lexical annotations and semantic embedding approaches to model the receptive vocabulary of L2 learners based on their lexical production during a writing task. The goal of the study is to examine the strengths and weaknesses of both approaches in understanding L2 lexical knowledge. Findings indicate that transformer approaches based on semantic embeddings outperform linguistic annotations and Word2vec models in predicting L2 learners’ vocabulary scores. The findings help to support the strength and accuracy of semantic-embedding approaches as well as their generalizability across tasks when compared to linguistic feature models. Limitations to semantic-embedding approaches, especially interpretability, are discussed.


Article metrics loading...

Loading full text...

Full text loading...


  1. Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F.
    (2020) Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 581, 82–115. 10.1016/j.inffus.2019.12.012
    https://doi.org/10.1016/j.inffus.2019.12.012 [Google Scholar]
  2. Balota, D. A., Cortese, M. J., Sergent-Marshall, S. D., Spieler, D. H., & Yap, M.
    (2004) Visual word recognition of single-syllable words. Journal of Experimental Psychology. General, 133(2), 283–316. 10.1037/0096‑3445.133.2.283
    https://doi.org/10.1037/0096-3445.133.2.283 [Google Scholar]
  3. Balota, D. A., Yap, M. J., Cortese, M. J., Hutchison, K. A., Kessler, B., Loftis, B., Neely, J. H., Nelson, D. L., Simpson, G. B., & Treiman, R.
    (2007) The English Lexicon Project. Behavior Research Methods, 391, 445–459. 10.3758/BF03193014
    https://doi.org/10.3758/BF03193014 [Google Scholar]
  4. Berger, C., Crossley, S., & Kyle, K.
    (2019) Using native-speaker psycholinguistic norms to predict lexical proficiency and development in second-language production. Applied Linguistics, 40 (1), 22–42. 10.1093/applin/amx005
    https://doi.org/10.1093/applin/amx005 [Google Scholar]
  5. Berger, C., Crossley, S., & Skalicky, S.
    (2019) Using lexical features to investigate second language lexical decision performance. Studies in Second Language Acquisition, 41(5), 911–935. 10.1017/S0272263119000019
    https://doi.org/10.1017/S0272263119000019 [Google Scholar]
  6. Biber, D.
    (1988) Variation across Speech and Writing. Cambridge University Press. 10.1017/CBO9780511621024
    https://doi.org/10.1017/CBO9780511621024 [Google Scholar]
  7. Biber, D., Gray, B., & Staples, S.
    (2016) Predicting Patterns of Grammatical Complexity Across Language Exam Task Types and Proficiency Levels. Applied Linguistics, 37(5), 639–668. 10.1093/applin/amu059
    https://doi.org/10.1093/applin/amu059 [Google Scholar]
  8. Bird, S., Klein, E., & Loper, E.
    (2009) Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Inc.
    [Google Scholar]
  9. BNC Consortium, The British National Corpus, XML Edition
    BNC Consortium, The British National Corpus, XML Edition (2007), Oxford Text Archive, hdl.handle.net/20.500.12024/2554
    [Google Scholar]
  10. Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T.
    (2017) Enriching Word Vectors with Subword Information. ArXiv:1607.04606 [Cs]. arxiv.org/abs/1607.04606. 10.1162/tacl_a_00051
    https://doi.org/10.1162/tacl_a_00051 [Google Scholar]
  11. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D.
    (2020) Language Models are Few-Shot Learners. ArXiv:2005.14165 [Cs]. arxiv.org/abs/2005.14165
    [Google Scholar]
  12. Brysbaert, M., & New, B.
    (2009) Moving beyond Kučera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 41(4), 977–990. 10.3758/BRM.41.4.977
    https://doi.org/10.3758/BRM.41.4.977 [Google Scholar]
  13. Brysbaert, M., Warriner, A. B., & Kuperman, V.
    (2014) Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46(3), 904–911. 10.3758/s13428‑013‑0403‑5
    https://doi.org/10.3758/s13428-013-0403-5 [Google Scholar]
  14. Clark, K., Khandelwal, U., Levy, O., & Manning, C. D.
    (2019) What Does BERT Look At? An Analysis of BERT’s Attention (arXiv:1906.04341). arXiv. arxiv.org/abs/1906.04341
    [Google Scholar]
  15. Cobb, T.
    (n.d.). Web Vocabprofile. https://www.lextutor.ca/
    [Google Scholar]
  16. Conrad, S.
    (2005) Corpus Linguistics and L2 Teaching. InHandbook of Research in Second Language Teaching and Learning. Routledge.
    [Google Scholar]
  17. Crossley, S. A., & Kyle, K.
    (2022) Managing Second Language Acquisition Data with Natural Language Processing Tools. InThe Open Handbook of Linguistic Data Management (pp.411–421). The MIT Press. 10.7551/mitpress/12200.003.0039
    https://doi.org/10.7551/mitpress/12200.003.0039 [Google Scholar]
  18. Crossley, S. A., Salsbury, T., McNamara, D. S., & Jarvis, S.
    (2011a) What Is Lexical Proficiency? Some Answers from Computational Models of Speech Data. TESOL Quarterly: A Journal for Teachers of English to Speakers of Other Languages and of Standard English as a Second Dialect, 45(1), 182–193. 10.5054/tq.2010.244019
    https://doi.org/10.5054/tq.2010.244019 [Google Scholar]
  19. (2011b) Predicting lexical proficiency in language learner texts using computational indices. Language Testing, 28(4), 561–580. 10.1177/0265532210378031
    https://doi.org/10.1177/0265532210378031 [Google Scholar]
  20. Crossley, S. A., & Skalicky, S.
    (2019) Examining Lexical Development in Second Language Learners: An Approximate Replication of Salsbury, Crossley & McNamara (2011) Language Teaching, 52(3), 385–405. 10.1017/S0261444817000362
    https://doi.org/10.1017/S0261444817000362 [Google Scholar]
  21. Crossley, S. A., Skalicky, S., Kyle, K., & Monteiro, K.
    (2019) Absolute frequency effects in second language lexical acquisition. Studies in Second Language Acquisition, 41(4), 721–744. 10.1017/S0272263118000268
    https://doi.org/10.1017/S0272263118000268 [Google Scholar]
  22. Crossley, S., Salsbury, T., & McNamara, D.
    (2009) Measuring L2 lexical growth using hypernymic relationships. Language Learning, 59(2), 307–334. 10.1111/j.1467‑9922.2009.00508.x
    https://doi.org/10.1111/j.1467-9922.2009.00508.x [Google Scholar]
  23. (2010) The Development of Polysemy and Frequency Use in English Second Language Speakers: Polysemy and Frequency Use in English L2 Speakers. Language Learning, 60(3), 573–605. 10.1111/j.1467‑9922.2010.00568.x
    https://doi.org/10.1111/j.1467-9922.2010.00568.x [Google Scholar]
  24. David, A.
    (2008) Vocabulary breadth in French L2 learners. The Language Learning Journal, 36(2), 167–180. 10.1080/09571730802389991
    https://doi.org/10.1080/09571730802389991 [Google Scholar]
  25. Davies, M.
    (2010) The Corpus of Contemporary American English as the first reliable monitor corpus of English. Literary and Linguistic Computing, 25(4), 447–464. 10.1093/llc/fqq018
    https://doi.org/10.1093/llc/fqq018 [Google Scholar]
  26. Devlin, J., Chang, M. -W., Lee, K., & Toutanova, K.
    (2019) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. ArXiv:1810.04805 [Cs]. arxiv.org/abs/1810.04805
    [Google Scholar]
  27. Došilović, F. K., Brčić, M., & Hlupić, N.
    (2018) Explainable artificial intelligence: A survey. 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 0210–0215. 10.23919/MIPRO.2018.8400040
    https://doi.org/10.23919/MIPRO.2018.8400040 [Google Scholar]
  28. Ellis, N. C.
    (2002) Frequency effects in language processing: A Review with Implications for Theories of Implicit and Explicit Language Acquisition. Studies in Second Language Acquisition, 24(2), 143–188. 10.1017/S0272263102002024
    https://doi.org/10.1017/S0272263102002024 [Google Scholar]
  29. Fellbaum, C.
    (1998) WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press. 10.7551/mitpress/7287.001.0001
    https://doi.org/10.7551/mitpress/7287.001.0001 [Google Scholar]
  30. Garner, J., & Crossley, S.
    (2018) A Latent Curve Model Approach to Studying L2 N-Gram Development. The Modern Language Journal, 102(3), 494–511. 10.1111/modl.12494
    https://doi.org/10.1111/modl.12494 [Google Scholar]
  31. Garner, J., Crossley, S., & Kyle, K.
    (2018) Beginning and intermediate L2 writer’s use of N-grams: An association measures study. International Review of Applied Linguistics in Language Teaching, 58(1), 51–74. 10.1515/iral‑2017‑0089
    https://doi.org/10.1515/iral-2017-0089 [Google Scholar]
  32. Goldberg, Y.
    (2019) Assessing BERT’s Syntactic Abilities (arXiv:1901.05287). arXiv. 10.48550/arXiv.1901.05287
    https://doi.org/10.48550/arXiv.1901.05287 [Google Scholar]
  33. Graesser, A. C., McNamara, D. S., Louwerse, M. M., & Cai, Z.
    (2004) Coh-Metrix: Analysis of text on cohesion and language. Behavior Research Methods, Instruments, & Computers, 36(2), 193–202. 10.3758/BF03195564
    https://doi.org/10.3758/BF03195564 [Google Scholar]
  34. Grant, L., & Ginther, A.
    (2000) Using Computer-Tagged Linguistic Features to Describe L2 Writing Differences. Journal of Second Language Writing, 9(2), 123–145. 10.1016/S1060‑3743(00)00019‑9
    https://doi.org/10.1016/S1060-3743(00)00019-9 [Google Scholar]
  35. Gunning, D., & Aha, D.
    (2019) DARPA’s Explainable Artificial Intelligence (XAI) Program. AI Magazine, 40(2), 44–58. 10.1609/aimag.v40i2.2850
    https://doi.org/10.1609/aimag.v40i2.2850 [Google Scholar]
  36. Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G. -Z.
    (2019) XAI-Explainable artificial intelligence. Science Robotics, 4(37), eaay7120. 10.1126/scirobotics.aay7120
    https://doi.org/10.1126/scirobotics.aay7120 [Google Scholar]
  37. Hashimoto, B. J., & Egbert, J.
    (2019) More Than Frequency? Exploring Predictors of Word Difficulty for Second Language Learners. Language Learning, 69(4), 839–872. 10.1111/lang.12353
    https://doi.org/10.1111/lang.12353 [Google Scholar]
  38. Huang, Y., Murakami, A., Alexopoulou, T., & Korhonen, A.
    (2018) Dependency parsing of learner English. International Journal of Corpus Linguistics, 23(1), 28–54. 10.1075/ijcl.16080.hua
    https://doi.org/10.1075/ijcl.16080.hua [Google Scholar]
  39. Ishikawa, S.
    (2013) The ICNALE and sophisticated contrastive interlanguage analysis of Asian learners of English. Learner Corpus Studies in Asia and the World, 11, 91–118.
    [Google Scholar]
  40. Ke, Z., & Ng, V.
    (2019) Automated Essay Scoring: A Survey of the State of the Art. 6300–6308.
    [Google Scholar]
  41. Kerz, E., Wiechmann, D., Qiao, Y., Tseng, E., & Ströbel, M.
    (2021) Automated Classification of Written Proficiency Levels on the CEFR-Scale through Complexity Contours and RNNs. Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, 199–209. https://aclanthology.org/2021.bea-1.21
    [Google Scholar]
  42. Kohavi, R., & John, G. H.
    (1995) Automatic Parameter Selection by Minimizing Estimated Error. InA. Prieditis & S. Russell. (Eds.), Machine Learning Proceedings 1995 (pp.304–312). Morgan Kaufmann. 10.1016/B978‑1‑55860‑377‑6.50045‑1
    https://doi.org/10.1016/B978-1-55860-377-6.50045-1 [Google Scholar]
  43. Koizumi, R., & In’nami, Y.
    (2013) Vocabulary Knowledge and Speaking Proficiency among Second Language Learners from Novice to Intermediate Levels. Journal of Language Teaching and Research, 4(5), 900–913. 10.4304/jltr.4.5.900‑913
    https://doi.org/10.4304/jltr.4.5.900-913 [Google Scholar]
  44. Kuhn, M.
    (2008) Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 281, 1–26. 10.18637/jss.v028.i05
    https://doi.org/10.18637/jss.v028.i05 [Google Scholar]
  45. Kuperman, V., Stadthagen-Gonzalez, H., & Brysbaert, M.
    (2012) Age-of-acquisition ratings for 30,000 English words. Behavior Research Methods, 44(4), 978–990. 10.3758/s13428‑012‑0210‑4
    https://doi.org/10.3758/s13428-012-0210-4 [Google Scholar]
  46. Kyle, K., & Crossley, S.
    (2016) The relationship between lexical sophistication and independent and source-based writing. Journal of Second Language Writing, 341, 12–24. 10.1016/j.jslw.2016.10.003
    https://doi.org/10.1016/j.jslw.2016.10.003 [Google Scholar]
  47. Kyle, K., & Crossley, S. A.
    (2015) Automatically Assessing Lexical Sophistication: Indices, Tools, Findings, and Application. TESOL Quarterly, 49(4), 757–786. 10.1002/tesq.194
    https://doi.org/10.1002/tesq.194 [Google Scholar]
  48. Kyle, K., Crossley, S., & Berger, C.
    (2018) The Tool for the Automatic Analysis of Lexical Sophistication (TAALES): Version 2.0. Behavior Research Methods, 50(3), 1030–1046. 10.3758/s13428‑017‑0924‑4
    https://doi.org/10.3758/s13428-017-0924-4 [Google Scholar]
  49. Landauer, 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(2), 211–240. 10.1037/0033‑295X.104.2.211
    https://doi.org/10.1037/0033-295X.104.2.211 [Google Scholar]
  50. Landauer, T. K., McNamara, D. S., Dennis, S., & Kintsch, W.
    (Eds.) (2007) Handbook of Latent Semantic Analysis. Psychology Press. 10.4324/9780203936399
    https://doi.org/10.4324/9780203936399 [Google Scholar]
  51. Lau, J. H., & Baldwin, T.
    (2016) An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation. Proceedings of the 1st Workshop on Representation Learning for NLP, 78–86. 10.18653/v1/W16‑1609
    https://doi.org/10.18653/v1/W16-1609 [Google Scholar]
  52. Laufer, B., & Nation, P.
    (1995) Vocabulary Size and Use: Lexical Richness in L2 Written Production. Applied Linguistics, 16(3), 307–322. 10.1093/applin/16.3.307
    https://doi.org/10.1093/applin/16.3.307 [Google Scholar]
  53. Le, Q. V., & Mikolov, T.
    (2014) Distributed Representations of Sentences and Documents. ArXiv:1405.4053 [Cs]. arxiv.org/abs/1405.4053
    [Google Scholar]
  54. Lemhöfer, K., Dijkstra, T., Schriefers, H., Baayen, R. H., Grainger, J., & Zwitserlood, P.
    (2008) Native language influences on word recognition in a second language: A megastudy. Journal of Experimental Psychology. Learning, Memory, and Cognition, 34(1), 12–31. 10.1037/0278‑7393.34.1.12
    https://doi.org/10.1037/0278-7393.34.1.12 [Google Scholar]
  55. Lu, Xiaofei
    (2012) The relationship of lexical richness to the quality of ESL learners’ oral narratives. The Modern Language Journal, 96(2), 190–208. 10.1111/j.1540‑4781.2011.01232_1.x
    https://doi.org/10.1111/j.1540-4781.2011.01232_1.x [Google Scholar]
  56. Lu, X., & Hu, R.
    (2021) Sense-aware lexical sophistication indices and their relationship to second language writing quality. Behavior Research Methods. 10.3758/s13428‑021‑01675‑6
    https://doi.org/10.3758/s13428-021-01675-6 [Google Scholar]
  57. McDonald, S. A., & Shillcock, R. C.
    (2001) Rethinking the Word Frequency Effect: The Neglected Role of Distributional Information in Lexical Processing. Language and Speech, 44(3), 295–322. 10.1177/00238309010440030101
    https://doi.org/10.1177/00238309010440030101 [Google Scholar]
  58. Meara, P.
    (1996) The dimensions of lexical competence. Performance and Competence in Second Language Acquisition, 351, 33–55.
    [Google Scholar]
  59. (2005a) Designing vocabulary tests for English. The Dynamics of Language Use: Functional and Contrastive Perspectives, 1401, 271. 10.1075/pbns.140.19mea
    https://doi.org/10.1075/pbns.140.19mea [Google Scholar]
  60. (2005b) Lexical frequency profiles: A Monte Carlo analysis. Applied Linguistics, 26(1), 32–47. 10.1093/applin/amh037
    https://doi.org/10.1093/applin/amh037 [Google Scholar]
  61. (2010) The relationship between L2 vocabulary knowledge and L2 vocabulary use. The Continuum Companion to Second Language Acquisition, 179–193.
    [Google Scholar]
  62. Meurers, D.
    (2012) Natural Language Processing and Language Learning. InThe Encyclopedia of Applied Linguistics. John Wiley & Sons, Ltd. 10.1002/9781405198431.wbeal0858
    https://doi.org/10.1002/9781405198431.wbeal0858 [Google Scholar]
  63. (2021) Natural Language Processing and Language Learning. InThe Encyclopedia of Applied Linguistics. John Wiley & Sons, Ltd. 10.1002/9781405198431.wbeal0858.pub2
    https://doi.org/10.1002/9781405198431.wbeal0858.pub2 [Google Scholar]
  64. Mikolov, T., Chen, K., Corrado, G., & Dean, J.
    (2013) Efficient Estimation of Word Representations in Vector Space. ArXiv:1301.3781 [Cs]. CitetononCRdoi:10.48550/ARXIV.1301.3781
    https://doi.org/Cite to nonCR doi: 10.48550/ARXIV.1301.3781 [Google Scholar]
  65. Milton, J.
    (2009) Measuring Second Language Vocabulary Acquisition. InMeasuring Second Language Vocabulary Acquisition. Multilingual Matters. 10.21832/9781847692092
    https://doi.org/10.21832/9781847692092 [Google Scholar]
  66. Moghadam, S. H., Zainal, Z., & Ghaderpour, M.
    (2012) A review on the important role of vocabulary knowledge in reading comprehension performance. Procedia-Social and Behavioral Sciences, 661, 555–563. 10.1016/j.sbspro.2012.11.300
    https://doi.org/10.1016/j.sbspro.2012.11.300 [Google Scholar]
  67. Monteiro, K. R., Crossley, S. A., & Kyle, K.
    (2020) In Search of New Benchmarks: Using L2 Lexical Frequency and Contextual Diversity Indices to Assess Second Language Writing. Applied Linguistics, 41(2), 280–300. 10.1093/applin/amy056
    https://doi.org/10.1093/applin/amy056 [Google Scholar]
  68. Morris, L., & Cobb, T.
    (2004) Vocabulary profiles as predictors of the academic performance of Teaching English as a Second Language trainees. System, 32(1), 75–87. 10.1016/j.system.2003.05.001
    https://doi.org/10.1016/j.system.2003.05.001 [Google Scholar]
  69. Mostafa, T., Crossley, S., & Kim, Y.
    (2021) Predictors of English as second language learners’ oral proficiency development in a classroom context. International Journal of Applied Linguistics, 31 (3), 526–548. 10.1111/ijal.12358
    https://doi.org/10.1111/ijal.12358 [Google Scholar]
  70. Nagy, W. E., & Scott, J. A.
    (2000) Vocabulary processes. InM. L. Kamil, P. Mosenthal, P. D. Pearson, & R. Barr. (Eds.), Handbook of reading research (Vol.31, pp.269–284). Mahwah, NJ: Earlbaum.
    [Google Scholar]
  71. Nation, P., & Beglar, D.
    (2007) A vocabulary size test. The Language Teacher, 31(7), 9–13.
    [Google Scholar]
  72. Nelson, D. L., McEvoy, C. L., & Schreiber, T. A.
    (2004) The University of South Florida free association, rhyme, and word fragment norms. Behavior Research Methods, Instruments, & Computers, 36(3), 402–407. 10.3758/BF03195588
    https://doi.org/10.3758/BF03195588 [Google Scholar]
  73. Ortega, L.
    (2016) Multi-competence in second language acquisition: inroads into the mainstream?InV. Cook & L. Wei. (Eds) The Cambridge Handbook of Linguistic Multi-competence. Cambridge University Press. 10.1017/CBO9781107425965.003
    https://doi.org/10.1017/CBO9781107425965.003 [Google Scholar]
  74. Paetzold, G., & Specia, L.
    (2016) Collecting and Exploring Everyday Language for Predicting Psycholinguistic Properties of Words. Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 1669–1679. https://aclanthology.org/C16-1157
    [Google Scholar]
  75. R Core Team
    R Core Team (2022) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
  76. Read, J.
    (1998) Validating a Test to Measure Depth of Vocabulary Knowledge. InValidation in Language Assessment. Routledge.
    [Google Scholar]
  77. Řehůřek, R., & Sojka, P.
    (2010) Software Framework for Topic Modelling with Large Corpora. Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, 45–50.
    [Google Scholar]
  78. Saito, K.
    (2020) Multi- or Single-Word Units? The Role of Collocation Use in Comprehensible and Contextually Appropriate Second Language Speech. Language Learning, 70(2), 548–588. 10.1111/lang.12387
    https://doi.org/10.1111/lang.12387 [Google Scholar]
  79. Sun, K., & Lu, X.
    (2021) Assessing Lexical Psychological Properties in Second Language Production: A Dynamic Semantic Similarity Approach. Frontiers in Psychology, 121, 672243. 10.3389/fpsyg.2021.672243
    https://doi.org/10.3389/fpsyg.2021.672243 [Google Scholar]
  80. Sundqvist, P.
    (2019) Commercial-off-the-shelf games in the digital wild and L2 learner vocabulary. Language Learning, 23(1), 27.
    [Google Scholar]
  81. Vanderbilt, Katia
    , “Developing and Testing Alternative Benchmarks of Lexical Sophistication: L2 Lexical Frequency, Semantic Context, and Word Recognition Indices.” Dissertation, Georgia State University 2020CitetononCRdoi:10.57709/18616934
    https://doi.org/Cite to nonCR doi: 10.57709/18616934
  82. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I.
    (2017) Attention Is All You Need. ArXiv:1706.03762 [Cs]. arxiv.org/abs/1706.03762
    [Google Scholar]
  83. Webb, S.
    (2008) Receptive and productive vocabulary sizes of L2 learners. Studies in Second Language Acquisition, 30(1), 79–95. 10.1017/S0272263108080042
    https://doi.org/10.1017/S0272263108080042 [Google Scholar]
  84. (2009) The Effects of Receptive and Productive Learning of Word Pairs on Vocabulary Knowledge. RELC Journal, 40(3), 360–376. 10.1177/0033688209343854
    https://doi.org/10.1177/0033688209343854 [Google Scholar]
  85. Wilson, M.
    (1988) MRC psycholinguistic database: Machine-usable dictionary, version 2.00. Behavior Research Methods, Instruments, & Computers, 20(1), 6–10. 10.3758/BF03202594
    https://doi.org/10.3758/BF03202594 [Google Scholar]
  86. Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Le Scao, T., Gugger, S., … Rush, A.
    (2020) Transformers: State-of-the-Art Natural Language Processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 38–45. 10.18653/v1/2020.emnlp‑demos.6
    https://doi.org/10.18653/v1/2020.emnlp-demos.6 [Google Scholar]
  87. Zaytseva, V., Miralpeix, I., & Pérez-Vidal, C.
    (2019) Because words matter: Investigating vocabulary development across contexts and modalities. Language Teaching Research, 136216881985297.
    [Google Scholar]
  88. Zhang, H., Chen, M., & Li, X.
    (2021) Developmental Features of Lexical Richness in English Writings by Chinese Beginner Learners. Frontiers in Psychology. 10.3389/fpsyg.2021.665988
    https://doi.org/10.3389/fpsyg.2021.665988 [Google Scholar]
  89. Zhu, J., Liapis, A., Risi, S., Bidarra, R., & Youngblood, G. M.
    (2018) Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation. 2018 IEEE Conference on Computational Intelligence and Games (CIG), 1–8. 10.1109/CIG.2018.8490433
    https://doi.org/10.1109/CIG.2018.8490433 [Google Scholar]

Data & Media loading...

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