A Neural Model for User Geolocation and Lexical Dialectology

Afshin Rahimi, Trevor Cohn, Timothy Baldwin


Abstract
We propose a simple yet effective text-based user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms. As part of our analysis of dialectal terms, we release DAREDS, a dataset for evaluating dialect term detection methods.
Anthology ID:
P17-2033
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
209–216
Language:
URL:
https://www.aclweb.org/anthology/P17-2033
DOI:
10.18653/v1/P17-2033
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-2033.pdf
Video:
 https://vimeo.com/234946757