Twitter Geolocation using Knowledge-Based Methods

Taro Miyazaki, Afshin Rahimi, Trevor Cohn, Timothy Baldwin


Abstract
Automatic geolocation of microblog posts from their text content is particularly difficult because many location-indicative terms are rare terms, notably entity names such as locations, people or local organisations. Their low frequency means that key terms observed in testing are often unseen in training, such that standard classifiers are unable to learn weights for them. We propose a method for reasoning over such terms using a knowledge base, through exploiting their relations with other entities. Our technique uses a graph embedding over the knowledge base, which we couple with a text representation to learn a geolocation classifier, trained end-to-end. We show that our method improves over purely text-based methods, which we ascribe to more robust treatment of low-count and out-of-vocabulary entities.
Anthology ID:
W18-6102
Volume:
Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
Month:
November
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | WNUT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7–16
Language:
URL:
https://www.aclweb.org/anthology/W18-6102
DOI:
10.18653/v1/W18-6102
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-6102.pdf