A Hierarchical Location Prediction Neural Network for Twitter User Geolocation

Binxuan Huang, Kathleen Carley


Abstract
Accurate estimation of user location is important for many online services. Previous neural network based methods largely ignore the hierarchical structure among locations. In this paper, we propose a hierarchical location prediction neural network for Twitter user geolocation. Our model first predicts the home country for a user, then uses the country result to guide the city-level prediction. In addition, we employ a character-aware word embedding layer to overcome the noisy information in tweets. With the feature fusion layer, our model can accommodate various feature combinations and achieves state-of-the-art results over three commonly used benchmarks under different feature settings. It not only improves the prediction accuracy but also greatly reduces the mean error distance.
Anthology ID:
D19-1480
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4732–4742
Language:
URL:
https://aclanthology.org/D19-1480
DOI:
10.18653/v1/D19-1480
Bibkey:
Cite (ACL):
Binxuan Huang and Kathleen Carley. 2019. A Hierarchical Location Prediction Neural Network for Twitter User Geolocation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4732–4742, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
A Hierarchical Location Prediction Neural Network for Twitter User Geolocation (Huang & Carley, EMNLP-IJCNLP 2019)
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PDF:
https://aclanthology.org/D19-1480.pdf