DOI:
10.18413/2313-8912-2023-9-1-0-1
Discourse complexity: driving forces of the new paradigm
Number of views: 704 (view statistics)
Количество скачиваний: 565
Information for citation:
Solovyev, V. D., Dascalu, M. and Solnyshkina, M. I. (2023). Discourse complexity: driving forces of the new paradigm, Research Result. Theoretical
and Applied Linguistics, 9 (1), 4-10. DOI: 10.18413/2313-8912-2023-9-1-0-1
While nobody left any comments to this publication.
You can be first.
Biber, D., Johansson, S., Leech, G., Conrad, S. and Finegan, E. (2021). Grammar of spoken and written English, John Benjamins, Amsterdam, Netherlands. https://doi.org/10.1075/z232(In English)
Botarleanu, R., Dascalu, M., Watanabe, M., Crossley, S. A. and McNamara, D. S. (2022). Age of Exposure 2.0: Estimating word complexity using Iterative models of word embeddings, Behavior Research Methods, 54, 3015-3042. https://doi.org/10.3758/s13428-022-01797-5 (In English)
Corlatescu, D., Ruseti, Ș, Dascalu, M. (2022). ReaderBench: Multilevel analysis of Russian text characteristics, Russian Journal of Linguistics, 26 (2), 342–370. https://doi.org/10.22363/2687-0088-30145(In English)
Crossley, S. A., Greenfield, J. and McNamara, D. S. (2008). Assessing Text Readability Using Cognitively Based Indices. TESOL Quarterly, 42 (3), 475–493. http://www.jstor.org/stable/40264479(In English)
Dascalu, M., Crossley, S. A., McNamara, D. S., Dessus, P. and Trausan-Matu, S. (2018). Please Readerbench this text: A multi-dimensional textual complexity assessment framework, in Craig, S. (ed.), Tutoring and Intelligent Tutoring Systems, Nova Science Publishers, Hauppauge, NY, 251–271. (In English)
Dascalu, M., McNamara, D. S., Crossley, S. A. and Trausan-Matu, S. (2016). Age of exposure: A model of word learning, in Zilberstein, S., Schuurmans, D. and Wellman, M. (eds.), Proceedings of the 30th Annual Meeting of the Association for the Advancement of Artificial Intelligence (AAAI'16), AAAI Press, Phoenix, AZ, 2928–2934. (In English)
Gatiyatullina, G., Solnyshkina, M., Solovyev, V., Danilov, A., Martynova, E. and Yarmakeev, I. (2020). Computing Russian Morphological distribution patterns using RusAC Online Server, Proceedings of the 13th International Conference on Developments in eSystems Engineering (DeSE), Liverpool, United Kingdom, 393-398. https://doi.org/10.1109/DeSE51703.2020.9450753(In English)
Ivanov, V. V. (2022). Sentence-level complexity in Russian: An evaluation of BERT and graph neural networks, Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.1008411(In English)
Kintsch, W. (1998). Comprehension: A paradigm for cognition, Cambridge University Press, Cambridge, MA. (In English)
Laposhina, A. N., Lebedeva, M. Yu. and Berlin Khenis, A. A. (2022). Word frequency and text complexity: an eye-tracking study of young Russian readers, Russian Journal of Linguistics, 26 (2), 493–514. https://doi.org/10.22363/2687-0088-30084(In Russian)
Lyashevskaya, O. I., Pyzhak, J. V., Vinogradova, O. N. (2022).Word-formation complexity: a learner corpus-based study, Russian Journal of Linguistics, 26 (2), 471–492. https://doi.org/10.22363/2687-0088-31187 (In English)
Martínez-Santiago, F., Torres-García, A. A., Montejo-Ráez, A. et al. (2023). The impact of reading fluency level on interactive information retrieval, Universal Access in the Information Society, 22, 51–67. https://doi.org/10.1007/s10209-021-00826-y (In English)
McNamara, D. S. (2011). Coh-Metrix: Its role in readability and the case for cohesion, Panel presentation for Exploring the Common Core standards’ approach to text complexity at 57th Annual Convention of the International Reading Association, Orlando, FL. (In English).
McNamara, D. S., Levinstein, I. B. and Boonthum, C. (2004). iSTART: Interactive strategy training for active reading and thinking, Behavior Research Methods, Instruments, & Computers, 36 (2), 222-233. https://doi:10.3758/bf03195567 (In English)
Putra, D. A. and Lukmana, I. (2017). Text complexity in senior high school English textbooks: A systemic functional perspective, Indonesian Journal of Applied Linguistics, 7 (2), 436-444. https://doi.org/10.17509/ijal.v7i2.8352(In English)
Shardlow, M. (2014). A Survey of Automated Text Simplification, International Journal of Advanced Computer Science and Applications, 4. http://dx.doi.org/10.14569/SpecialIssue.2014.040109 (In English)
Sharoff, S. A. (2022). What neural networks know about linguistic complexity, Russian Journal of Linguistics, 26 (2), 371–390. https://doi.org/10.22363/2687-0088-30178 (In English)
Sherman, L. A. (1893). Analytics of Literature, a manual for the objective study of English prose and poetry, Ginn & Company, Boston, MA. (In English)
Solnyshkina, M. I. and Kisel'nikov, A. S. (2015). Slozhnost' teksta: Ehtapy izucheniya v otechestvennom prikladnom yazykoznanii [Text complexity: Stages of study in domestic applied linguistics], Vestnik Tomskogo gosudarstvennogo universiteta. Filologiya, 6, 86–99. (In Russian)
Solnyshkina, M. I., Solovyev, V. D., Gafiyatova, E. V. and Martynova, E. V. (2022). Text complexity as interdisciplinary problem, Voprosy Kognitivnoy Lingvistiki, 1, 18-39. (In Russian)
Solnyshkina, M. I., Harkova, E. V. and Kazachkova, M. B. (2020). The structure of cross-linguistic differences: Meaning and context of ‘readability’ and its Russian equivalent ‘chitabelnost’, Journal of Language and Education, 6 (1), 103-119. http://doi.org/10.17323/jle.2020.7176 (In English)
Wenger, M. J. and Payne, D. G. (1996). Comprehension and Retention of Nonlinear Text: Considerations of Working Memory and Material-Appropriate Processing, The American Journal of Psychology, 109 (1), 93–130. https://doi.org/10.2307/1422929 (In English)
Wolfe, M. B. W., Schreiner, M. E., Rehder, B., Laham, D, Foltz, P. W., Kintsch, W. and Landauer, T. K. (1998). Learning from text: Matching readers and texts by latent semantic analysis, Discourse Processes, 25, 309-336. (In English)