Deep Learning Based Topic Classification for Sensitivity Assignment to Personal Data

Apdullah Yayık, Hasan Apik, Ayşe Tosun

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

Özet

This study aims to understand non-linear relations in Turkish textual contents to predict their topic with the help of machine learning models and to discuss contributions of the models to compliance with Personal Data Protection Rule (PDPR). Since the exponential growth of concerns in personal data processing, it has been a necessity to know the topic of the textual contents to interpret their usage in which environment they are located. The topic of the document is a piece of inclusive information presented together with all the data in its content, by this reason the categories of personal data defined in the PDPR may vary due to semantic impacts from its environment in which it is being processed. Many experiments are conducted with the fasttext model employing logistic regression and deep bidirectional transformers (BERT) models having attention layers. Model performances and statistical post-hoc test results on the model predictions are analyzed, then model deployment together with the industrial usage are discussed. As a result, a fasttext model having a macro-averaged F1-measure of 94.73 ± 0.67% is created and integrated into production efficiently.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıProceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar292-297
Sayfa sayısı6
ISBN (Elektronik)9781665429085
DOI'lar
Yayın durumuYayınlandı - 2021
Etkinlik6th International Conference on Computer Science and Engineering, UBMK 2021 - Ankara, Turkey
Süre: 15 Eyl 202117 Eyl 2021

Yayın serisi

AdıProceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021

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???event.eventtypes.event.conference???6th International Conference on Computer Science and Engineering, UBMK 2021
Ülke/BölgeTurkey
ŞehirAnkara
Periyot15/09/2117/09/21

Bibliyografik not

Publisher Copyright:
© 2021 IEEE

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