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https://doi.org/10.21256/zhaw-4850| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | Twist Bytes : German dialect identification with data mining optimization |
| Authors: | Benites de Azevedo e Souza, Fernando Grubenmann, Ralf von Däniken, Pius von Grünigen, Dirk Deriu, Jan Milan Cieliebak, Mark |
| DOI: | 10.21256/zhaw-4850 |
| Proceedings: | Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018) |
| Page(s): | 218 |
| Pages to: | 227 |
| Conference details: | 27th International Conference on Computational Linguistics (COLING 2018), Santa Fe, August 20-26, 2018 |
| Issue Date: | 2018 |
| Publisher / Ed. Institution: | VarDial |
| Language: | English |
| Subjects: | Dialect recognition; Text classification; Shared task; Swiss german |
| Subject (DDC): | 410.285: Computational linguistics 430: German |
| Abstract: | We describe our approaches used in the German Dialect Identification (GDI) task at the VarDial Evaluation Campaign 2018. The goal was to identify to which out of four dialects spoken in German speaking part of Switzerland a sentence belonged to. We adopted two different metaclassifier approaches and used some data mining insights to improve the preprocessing and the meta-classifier parameters. Especially, we focused on using different feature extraction methods and how to combine them, since they influenced the performance very differently of the system. Our system achieved second place out of 8 teams, with a macro averaged F-1 of 64.6%. We also participated on the surprise dialect task with a multi-label approach. |
| URI: | http://www.aclweb.org/anthology/W18-3925 https://digitalcollection.zhaw.ch/handle/11475/11222 |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) |
| Appears in collections: | Publikationen School of Engineering |
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|---|---|---|---|---|
| 2018_Benites_Twist_Bytes_German_Dialect_Identification_with_data_mining_optimization.pdf | 250.32 kB | Adobe PDF |  View/Open |
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Benites de Azevedo e Souza, F., Grubenmann, R., von Däniken, P., von Grünigen, D., Deriu, J. M., & Cieliebak, M. (2018). Twist Bytes : German dialect identification with data mining optimization [Conference paper]. Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018), 218–227. https://doi.org/10.21256/zhaw-4850
Benites de Azevedo e Souza, F. et al. (2018) ‘Twist Bytes : German dialect identification with data mining optimization’, in Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018). VarDial, pp. 218–227. Available at: https://doi.org/10.21256/zhaw-4850.
F. Benites de Azevedo e Souza, R. Grubenmann, P. von Däniken, D. von Grünigen, J. M. Deriu, and M. Cieliebak, “Twist Bytes : German dialect identification with data mining optimization,” in Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018), 2018, pp. 218–227. doi: 10.21256/zhaw-4850.
BENITES DE AZEVEDO E SOUZA, Fernando, Ralf GRUBENMANN, Pius VON DÄNIKEN, Dirk VON GRÜNIGEN, Jan Milan DERIU und Mark CIELIEBAK, 2018. Twist Bytes : German dialect identification with data mining optimization. In: Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018) [online]. Conference paper. VarDial. 2018. S. 218–227. Verfügbar unter: http://www.aclweb.org/anthology/W18-3925
Benites de Azevedo e Souza, Fernando, Ralf Grubenmann, Pius von Däniken, Dirk von Grünigen, Jan Milan Deriu, and Mark Cieliebak. 2018. “Twist Bytes : German Dialect Identification with Data Mining Optimization.” Conference paper. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018), 218–27. VarDial. https://doi.org/10.21256/zhaw-4850.
Benites de Azevedo e Souza, Fernando, et al. “Twist Bytes : German Dialect Identification with Data Mining Optimization.” Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018), VarDial, 2018, pp. 218–27, https://doi.org/10.21256/zhaw-4850.
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