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https://doi.org/10.21256/zhaw-21551| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | ZHAW-InIT : social media geolocation at VarDial 2020 |
| Authors: | Benites de Azevedo e Souza, Fernando Hürlimann, Manuela von Däniken, Pius Cieliebak, Mark |
| et. al: | No |
| DOI: | 10.21256/zhaw-21551 |
| Proceedings: | Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects |
| Editors of the parent work: | Zampieri, Marcos Nakov, Preslav Ljubešić, Nikola Tiedemann, Jörg Scherrer, Yves |
| Pages: | 254 |
| Pages to: | 264 |
| Conference details: | Workshop on NLP for Similar Languages, Varieties and Dialects, Barcelona (Spain), online, 13 December 2020 |
| Issue Date: | 13-Dec-2020 |
| Publisher / Ed. Institution: | International Committee on Computational Linguistics (ICCL) |
| ISBN: | 978-1-952148-47-7 |
| Language: | English |
| Subject (DDC): | 410.285: Computational linguistics |
| Abstract: | We describe our approaches for the Social Media Geolocation (SMG) task at the VarDial Evaluation Campaign 2020. The goal was to predict geographical location (latitudes and longitudes) given an input text. There were three subtasks corresponding to German-speaking Switzerland (CH), Germany and Austria (DE-AT), and Croatia, Bosnia and Herzegovina, Montenegro and Serbia (BCMS). We submitted solutions to all subtasks but focused our development efforts on the CH subtask, where we achieved third place out of 16 submissions with a median distance of 15.93 km and had the best result of 14 unconstrained systems. In the DE-AT subtask, we ranked sixth out of ten submissions (fourth of 8 unconstrained systems) and for BCMS we achieved fourth place out of 13 submissions (second of 11 unconstrained systems). |
| URI: | https://www.aclweb.org/anthology/2020.vardial-1.24 https://digitalcollection.zhaw.ch/handle/11475/21551 |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | School of Engineering |
| Organisational Unit: | Centre for Artificial Intelligence (CAI) Institute of Applied Information Technology (InIT) |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2020_Benites_etal_ZHAW_InIT_VarDial2020.pdf | 512.81 kB | Adobe PDF |  View/Open |
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