@inproceedings{benites-etal-2020-zhaw,
title = "{ZHAW}-{I}n{IT} - Social Media Geolocation at {V}ar{D}ial 2020",
author = {Benites, Fernando and
H{\"u}rlimann, Manuela and
von D{\"a}niken, Pius and
Cieliebak, Mark},
editor = {Zampieri, Marcos and
Nakov, Preslav and
Ljube{\v{s}}i{\'c}, Nikola and
Tiedemann, J{\"o}rg and
Scherrer, Yves},
booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics (ICCL)",
url = "https://aclanthology.org/2020.vardial-1.24",
pages = "254--264",
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).",
}
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%0 Conference Proceedings
%T ZHAW-InIT - Social Media Geolocation at VarDial 2020
%A Benites, Fernando
%A Hürlimann, Manuela
%A von Däniken, Pius
%A Cieliebak, Mark
%Y Zampieri, Marcos
%Y Nakov, Preslav
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Scherrer, Yves
%S Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2020
%8 December
%I International Committee on Computational Linguistics (ICCL)
%C Barcelona, Spain (Online)
%F benites-etal-2020-zhaw
%X 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).
%U https://aclanthology.org/2020.vardial-1.24
%P 254-264
Markdown (Informal)
[ZHAW-InIT - Social Media Geolocation at VarDial 2020](https://aclanthology.org/2020.vardial-1.24) (Benites et al., VarDial 2020)
ACL
- Fernando Benites, Manuela Hürlimann, Pius von Däniken, and Mark Cieliebak. 2020. ZHAW-InIT - Social Media Geolocation at VarDial 2020. In Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects, pages 254–264, Barcelona, Spain (Online). International Committee on Computational Linguistics (ICCL).