Please use this identifier to cite or link to this item: 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 SizeFormat 
2020_Benites_etal_ZHAW_InIT_VarDial2020.pdf512.81 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.