Detecting Online Risks and Supportive Interaction in Instant
Messenger Conversations using Czech Transformers

D 2021

Detecting Online Risks and Supportive Interaction in Instant Messenger Conversations using Czech Transformers

SOTOLÁŘ, Ondřej, Jaromír PLHÁK, Michal TKACZYK, Michaela LEBEDÍKOVÁ, David ŠMAHEL et. al.

Základní údaje

Originální název

Detecting Online Risks and Supportive Interaction in Instant Messenger Conversations using Czech Transformers

Autoři

SOTOLÁŘ, Ondřej (203 Česká republika, garant, domácí), Jaromír PLHÁK (203 Česká republika, domácí), Michal TKACZYK (203 Česká republika, domácí), Michaela LEBEDÍKOVÁ (203 Česká republika, domácí) a David ŠMAHEL (203 Česká republika, domácí)

Vydání

Brno, Recent Advances in Slavonic Natural Language Processing (RASLAN 2021), od s. 19-28, 10 s. 2021

Nakladatel

Tribun EU

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

Domovská stránka workshopu Full text PDF

Kód RIV

RIV/00216224:14330/21:00119420

Organizační jednotka

Fakulta informatiky

ISBN

978-80-263-1670-1

ISSN

Klíčová slova anglicky

Online Risks; Supportive Interaction; Facebook Messenger; Text Classification

Změněno: 15. 5. 2024 02:07, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

We present a comparison of state-of-the-art models for text clas- sification of Online Risks and Supportive Interaction in anonymized In- stant Messenger conversations held in Czech. We compare the transformer models Czert, RobeCzech, and FERNET-C5 with the Fasttext classifier as a baseline. For the comparison, we build a novel dataset with five sub- categories for the Online Risks and five for the Supportive Interaction. We solve the balanced classification problem achieving 75.44 - 89.66 F1 score depending on the category. Our results show that the transformer models perform consistently better than the baseline.

Návaznosti

GX19-27828X, projekt VaV

Název: Pohled do budoucnosti: Porozumění vlivu technologií na “well-being” adolescentů (Akronym: FUTURE)

Investor: Grantová agentura ČR, Modelling the future: Understanding the impact of technology on adolescent’s well-being

Citovat

SOTOLÁŘ, Ondřej, Jaromír PLHÁK, Michal TKACZYK, Michaela LEBEDÍKOVÁ a David ŠMAHEL. Detecting Online Risks and Supportive Interaction in Instant Messenger Conversations using Czech Transformers. In Horák, Rychlý, Rambousek. Recent Advances in Slavonic Natural Language Processing (RASLAN 2021). Brno: Tribun EU, 2021, s. 19-28. ISBN 978-80-263-1670-1.

@inproceedings{1809721,
   author = {Sotolář, Ondřej and Plhák, Jaromír and Tkaczyk, Michal and Lebedíková, Michaela and Šmahel, David},
   address = {Brno},
   booktitle = {Recent Advances in Slavonic Natural Language Processing (RASLAN 2021)},
   editor = {Horák, Rychlý, Rambousek},
   keywords = {Online Risks; Supportive Interaction; Facebook Messenger; Text Classification},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Brno},
   isbn = {978-80-263-1670-1},
   pages = {19-28},
   publisher = {Tribun EU},
   title = {Detecting Online Risks and Supportive Interaction in Instant Messenger Conversations using Czech Transformers},
   url = {https://raslan2021.nlp-consulting.net/},
   year = {2021}
}

TY  - JOUR
ID  - 1809721
AU  - Sotolář, Ondřej - Plhák, Jaromír - Tkaczyk, Michal - Lebedíková, Michaela - Šmahel, David
PY  - 2021
TI  - Detecting Online Risks and Supportive Interaction in Instant Messenger Conversations using Czech Transformers
PB  - Tribun EU
CY  - Brno
SN  - 9788026316701
KW  - Online Risks
KW  - Supportive Interaction
KW  - Facebook Messenger
KW  - Text Classification
UR  - https://raslan2021.nlp-consulting.net/
N2  - We present a comparison of state-of-the-art models for text clas- sification of Online Risks and Supportive Interaction in anonymized In- stant Messenger conversations held in Czech. We compare the transformer models Czert, RobeCzech, and FERNET-C5 with the Fasttext classifier as a baseline. For the comparison, we build a novel dataset with five sub- categories for the Online Risks and five for the Supportive Interaction. We solve the balanced classification problem achieving 75.44 - 89.66 F1 score depending on the category. Our results show that the transformer models perform consistently better than the baseline.
ER  -

SOTOLÁŘ, Ondřej, Jaromír PLHÁK, Michal TKACZYK, Michaela LEBEDÍKOVÁ a David ŠMAHEL. Detecting Online Risks and Supportive Interaction in Instant Messenger Conversations using Czech Transformers. In Horák, Rychlý, Rambousek. \textit{Recent Advances in Slavonic Natural Language Processing (RASLAN 2021)}. Brno: Tribun EU, 2021, s.~19-28. ISBN~978-80-263-1670-1.

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