Modeling the meaning of individual words using cultural cartography and keystroke dynamics

Modeling the meaning of individual words using cultural cartography and keystroke dynamics

Автор: Litvinova T.A., Dekhnich O.V.

Журнал: Интеграция образования @edumag-mrsu

Рубрика: Образование и культура

Статья в выпуске: 4 (117), 2024 года.

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Introduction. Revealing the psychologically real, individual meaning of the word as opposed to its dictionary meaning is the important task since such knowledge is crucial for effective communication. This is especially true for the words which denote key ideas and concepts of the culture. The word association experiment has been one of the most used methodologies to examine individual meaning of the word but it has been heavily criticized because of its subjectivity. In some of the recent works, data from language models and methods of vector semantics have been used to solve this problem. However, firstly, the very set of the features by which the meaning of the word is described is not uniform, which does not allow for a comparison of the results, and, secondly, some other types of data related to word production (i.e., behavioral data) are typically not taken into account. The aim of the present study is to reveal and systematically describe individual differences in the psychologically real meaning of the particular key words of the Russian culture using a new methodology which could be applied to any word association task. We propose to analyze data of different types (semantic features and keystroke dynamics markers) obtained during word association production to reveal individual differences in the word meaning.

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Cultural semantics, word meaning, keystroke dynamics, word associations, distributional semantics, language models, multidimensional analysis, r studio

Короткий адрес: https://sciup.org/147247095

IDR: 147247095   |   DOI: 10.15507/1991-9468.117.028.202404.624-640

Список литературы Modeling the meaning of individual words using cultural cartography and keystroke dynamics

  • Lenci A. Distributional Models of Word Meaning. Annual Review of Linguistics. 2018;4:151-171. https:// doi.org/10.1146/annurev-linguistics-030514-125254
  • Boleda G. Distributional Semantics and Linguistic Theory. Annual Review of Linguistics. 2020;6:213-234. https://doi.org/10.1146/annurev-linguistics-011619-030303
  • Stoltz D.S., Taylor M.A. Cultural Cartography with Word Embeddings. Poetics. 2021;88:101567. https:// doi.org/10.1016/j.poetic.2021.101567
  • Lee M., Martin J.L. Coding, Counting and Cultural Cartography. American Journal of Cultural Sociology. 2015;3:1-33. https://doi.org/10.1057/ajcs.2014.13
  • 9 Vygotsky L.S. Vol. 1. Problems of General Psychology, Including the Volume Thinking and Speech (Cognition and Language: A Series in Psycholinguistics). P. 221.
  • Kozlowski A., Taddy M., Evans J.A. The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings. American Sociological Review. 2019;84(5):905-949. https://doi. org/10.1177/0003122419877135
  • Litvinova T., Panicheva P. Individual Differences in the Associative Meaning of a Word Through the Lens of the Language Model and Semantic Differential. Research Result. Theoretical and Applied Linguistics. 2024;10(1):61-93. (In Russ., abstract in Eng.) https://doi.org/10.18413/2313-8912-2024-10-1-0-5
  • Wengelin A., Johansson V. Investigating Writing Processes with Keystroke Logging. In: Kruse O., Rapp C., Ansonet C.M., Benetos K., Cotos E., Devitt A., et al. (eds) Digital Writing Technologies in Higher Education. Cham: Springer; 2023. p. 405-420. https://doi.org/10.1007/978-3-031-36033-6_25
  • Torrance M., Rianne C. Methods for Studying the Writing Time-Course. Reading and Writing. 2024;37:239-251. https://doi.org/10.1007/s11145-023-10490-8
  • Vandermeulen N., Van Steendam E., De Maeyer S., Rijlaarsdam G. Writing Process Feedback Based on Keystroke Logging and Comparison with Exemplars: Effects on the Quality and Process of Synthesis Texts. Written Communication. 2023;40(1):90-144. https://doi.org/10.1177/07410883221127998
  • Ismail M.G., Salem M.A.-M., Abd El Ghany M.A., Aldakheel E.A., Abbas S. Outlier Detection for Keystroke Biometric User Authentication. PeerJ Computer Science. 2024;10:e2086. https://doi.org/10.7717/ peerj-cs.2086
  • Acien A., Calcagno N., Burke K.M., Mondesire-Crump I., Holmes A.A., Mruthik S., et al. A Novel Digital Tool for Detection and Monitoring of Amyotrophic Lateral Sclerosis Motor Impairment and Progression via Keystroke Dynamics. Scientific Reports. 2024;14:16851. https://doi.org/10.1038/s41598-024-67940-8
  • Borj P.R., Bours P. Detecting Liars in Chats Using Keystroke Dynamics. In: Proceedings of the 2019 3rd International Conference on Biometric Engineering and Applications (ICBEA 2019). New York: Association for Computing Machinery; 2019. p. 1-6. https://doi.org/10.1145/3345336.3345337
  • MacNiven S., Tench R. Keystrokes: A Practical Exploration of Semantic Drift in Timed Word Association Tasks. PLoS ONE. 2024;19(7):e0305568. https://doi.org/10.1371/journal.pone.0305568
  • Utsumi A. Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis. Cognitive Science. 2020;44(6):e12844. https://doi.org/10.1111/cogs.12844
  • Johns B.T. Determining the Relativity of Word Meanings through the Construction of Individualized Models of Semantic Memory. Cognitive Science. 2024;48(2):e13413. https://doi.org/10.1111/cogs.13413
  • Thompson B., Roberts S.G., Lupyan G. Cultural Influences on Word Meanings Revealed through Large-Scale Semantic Alignment. Nature Human Behaviour. 2020;4:1029-1038. https://doi.org/10.1038/ s41562-020-0924-8
  • Wang X., Bi Y. Idiosyncratic Tower of Babel: Individual Differences in Word-Meaning Representation Increase as Word Abstractness Increases. Psychological Science. 2021;32(10):1617-1635. https://doi. org/10.1177/09567976211003877
  • Johns B. Computing Word Meanings by Aggregating Individualized Distributional Models: Wisdom of the Crowds in Lexical Semantic Memory. Cognitive Systems Research. 2023;80:90-102. https://doi.org/10.1016/j. cogsys.2023.02.009
  • Li P., Schloss B., Follmer D.J. Speaking Two "Languages" in America: A Semantic Space Analysis of How Presidential Candidates and Their Supporters Represent Abstract Political Concepts Differently. Behavior Research Methods. 2017;49:1668-1685. https://doi.org/10.3758/s13428-017-0931-5
  • Diallo A., Furnkranz J. Unsupervised Alignment of Distributional Word Embeddings. In: Bergmann R., Malburg L., Rodermund S.C., Timm I.J. (eds) KI 2022: Advances in Artificial Intelligence. Cham.: Springer; 2022. p. 60-74. https://doi.org/10.1007/978-3-031-15791-2_7
  • Stoltz D.S., Taylor M.A., Dudley J.S.K. A Tool Kit for Relation Induction in Text Analysis. Sociological Methods and Research. 2024. https://doi.org/10.1177/00491241241233242
  • Grand G., Blank I.A., Pereira F., Fedorenko E. Semantic Projection Recovers Rich Human Knowledge of Multiple Object Features from Word Embeddings. Nature Human Behaviour. 2022;6:975-987. https://doi. org/10.1038/s41562-022-01316-8
  • Binder J.R., Conant L.L., Humphries C.J., Fernandino L., Simons S.B., Aguilar M., et al. Toward a Brain-Based Componential Semantic Representation. Cognitive Neuropsychology. 2016;33(3-4):130-174. https://doi. org/10.1080/02643294.2016.1147426
  • Chersoni E., Santus E., Huang C., Lenci A. Decoding Word Embeddings with Brain-Based Semantic Features. Computational Linguistics. 2021;47(3):663-698. https://doi.org/10.1162/coli_a_00412
  • Aldridge M., Fontaine L., Bowen N., Smith T. A New Perspective on Word Association: How Keystroke Logging Informs Strength of Word Association. WORD. 2018;64(4):218-234. https://doi.org/10.1080/00437956. 2018.1535365
  • Ivanouw J. Stimulus Affectivity of the Danish Word Association Test as Measured by Response Heterogeneity and Rasch Scaled Number of Prolonged Reaction Times. Scandinavian Journal of Psychology. 2006;47(1):51-59. https://doi.org/10.1111/j.l467-9450.2006.00492.x
  • Zagorovskaya O.V. Key Words of the Russian Culture in the Aspects of the New Realities of School Language Education. Sovremennye problemy lingvistiki i metodiki prepodavaniya russkogo yazyka v vuze i shkole. 2018;(28):73-78. (In Russ., abstract in Eng.) Available at: https://new-journal.ru/nomer/28-nomer/ (accessed 09.08.2024).
  • Hills T.T., Jones M.N., Todd P.M. Optimal Foraging in Semantic Memory. Psychological Review. 2012;119(2):431-440. https://doi.org/10.1037/a0027373
  • Litvinova T., Zavarzina V., Panicheva P., Lyubova S., Mamaev I. RuPersWordAssociation: A New Dataset to Study Individual Association Behavior. In: Proceedings of the International Conference "Internet and Modern Society" (IMS-2024) (in press).
  • Stoltz D.S., Taylor M.A. text2map: R Tools for Text Matrices. Journal of Open Source Software. 2022;7(72):3741. https://doi.org/10.21105/joss.03741
  • Taylor M.A., Stoltz D.S. Integrating Semantic Directions with Concept Mover's Distance to Measure Binary Concept Engagement. Journal of Computational Social Science. 2021;4:231-242. https://doi.org/10.1007/ s42001 -020-00075-8
  • Stoltz D.S., Taylor M.A. Concept Mover's Distance: Measuring Concept Engagement via Word Em-beddings in Texts. Journal of Computational Social Science. 2019;2:293-313. https://doi.org/10.1007/s42001-019-00048-6
  • Miklashevsky A. Perceptual Experience Norms for 506 Russian Nouns: Modality Rating. Spatial Localization, Manipulability, Imageability and Other Variables. Journal of Psycholinguistic Research. 2018;47:641-661. https://doi.org/10.1007/s10936-017-9548-1
  • Panicheva P., Litvinova T. Matching LIWC with Russian Thesauri: An Exploratory Study. In: Filchenkov A., Kauttonen J., Pivovarova L. (eds) Artificial Intelligence and Natural Language. AINL 2020. Communications in Computer and Information Science. Cham.: Springer; 2020. p. 181-195. https://doi.org/10.1007/978-3-030-59082-6_14
  • Wengelin Ä. Examining Pauses in Writing: Theory, Methods and Empirical Data. In: Sullivan K.P., Lind-gren E. (eds) Computer Key-Stroke Logging and Writing. Leiden: Brill; 2006. Vol. 18. p. 107-130. https://doi. org/10.1163/9780080460932_008
  • Bates D., Mächler M., Bolker B., Walker S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software. 2015;67(1):1-48. https://doi.org/10.18637/jss.v067.i01
  • Le S., Josse J., Husson F. FactoMineR: A Package for Multivariate Analysis. Journal of Statistical Software. 2008;25(1):1-18. https://doi.org/10.18637/jss.v025.i01
  • Tomczak M., Tomczak E. The Need to Report Effect Size Estimates Revisited. An Overview of Some Recommended Measures of Effect Size. Trends in Sport Sciences. 2014;21(1):19-25. Available at: https:// tss.awf.poznan.pl/The-need-to-report-effect-size-estimates-revisited-An-overview-of-some-recommend-ed,188960,0,2.html (accessed 09.08.2024).
  • Wang X., Men W., Gao J., Caramazza A., Bi Y. Two Forms of Knowledge Representations in the Human Brain. Neuron. 2020;107(2):383-393. https://doi.org/10.1016/j.neuron.2020.04.010
  • Warriner A.B., Kuperman V., Brysbaert M. Norms of Valence, Arousal, and Dominance for 13,915 English Lemmas. Behavior Research Methods. 2013;45:1191-1207. https://doi.org/10.3758/s13428-012-0314-x
  • Galac A., Zayniev D. Paths of Linguistic Synesthesia across Cultures: A Lexical Analysis of Conventionalized Cross-Sensory Meaning Extensions in Europe and Central Asia. Cognitive Linguistic Studies. 2023;10(2):450-479. https://doi.org/10.1075/cogls.00108.gal
  • Mroczko-Wqsowicz A., Nikolic D. Semantic Mechanisms May be Responsible for Developing Synesthesia. Frontiers in Human Neuroscience. 2014;8:509. https://doi.org/10.3389/fnhum.2014.00509
  • Litvinova T.A., Zavarzina V.A., Kotlyarova E.S., Lyubova S.G. Mapping the Field of Word Association Research Using Text Mining Approach. In: ITCC '23: Proceedings of the 2023 5th International Conference on Information Technology and Computer Communications. New York: Association for Computing Machinery; 2023. p. 90-98. https://doi.org/10.1145/3606843.3606858
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