Abstract
We present a quantitative analysis of human word association pairs and study the types of relations presented in the associations. We put our main focus on the correlation between response types and respondent characteristics such as occupation and gender by contrasting syntagmatic and paradigmatic associations. Finally, we propose a personalised distributed word association model and show the importance of incorporating demographic factors into the models commonly used in natural language processing.
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Notes
- 1.
The dataset is available at http://github.com/ivri/RusAssoc.
- 2.
i.e. associative networks are scale-free.
- 3.
People in psycholinguistics typically assume that the core of the verbal associations becomes stable and does not significantly change after the age of 18.
- 4.
We used mystem [21] to extract lemmas.
- 5.
- 6.
- 7.
With p-value\(<0.001\).
- 8.
The dataset is quite balanced and we have roughly the same number of questionnaires for both male and female participants.
- 9.
We used the model implementation from https://bitbucket.org/omerlevy/hyperwords. We set the size of the context window to 1 (left and right words), embedding size to 100, context distribution smoothing of 0.75, token threshold value of 5, all the other parameters were left with their default values.
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Acknowledgments
We would like to thank all reviewers for their valuable comments and suggestions for future research directions. The first author was supported by the Melbourne International Research Scholarship (MIRS).
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Vylomova, E., Shcherbakov, A., Philippovich, Y., Cherkasova, G. (2018). Men Are from Mars, Women Are from Venus: Evaluation and Modelling of Verbal Associations. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2017. Lecture Notes in Computer Science(), vol 10716. Springer, Cham. https://doi.org/10.1007/978-3-319-73013-4_10
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