@inproceedings{lau-etal-2017-end,
title = "End-to-end Network for {T}witter Geolocation Prediction and Hashing",
author = "Lau, Jey Han and
Chi, Lianhua and
Tran, Khoi-Nguyen and
Cohn, Trevor",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-1075",
pages = "744--753",
abstract = "We propose an end-to-end neural network to predict the geolocation of a tweet. The network takes as input a number of raw Twitter metadata such as the tweet message and associated user account information. Our model is language independent, and despite minimal feature engineering, it is interpretable and capable of learning location indicative words and timing patterns. Compared to state-of-the-art systems, our model outperforms them by 2{\%}-6{\%}. Additionally, we propose extensions to the model to compress representation learnt by the network into binary codes. Experiments show that it produces compact codes compared to benchmark hashing algorithms. An implementation of the model is released publicly.",
}
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%0 Conference Proceedings
%T End-to-end Network for Twitter Geolocation Prediction and Hashing
%A Lau, Jey Han
%A Chi, Lianhua
%A Tran, Khoi-Nguyen
%A Cohn, Trevor
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F lau-etal-2017-end
%X We propose an end-to-end neural network to predict the geolocation of a tweet. The network takes as input a number of raw Twitter metadata such as the tweet message and associated user account information. Our model is language independent, and despite minimal feature engineering, it is interpretable and capable of learning location indicative words and timing patterns. Compared to state-of-the-art systems, our model outperforms them by 2%-6%. Additionally, we propose extensions to the model to compress representation learnt by the network into binary codes. Experiments show that it produces compact codes compared to benchmark hashing algorithms. An implementation of the model is released publicly.
%U https://aclanthology.org/I17-1075
%P 744-753
Markdown (Informal)
[End-to-end Network for Twitter Geolocation Prediction and Hashing](https://aclanthology.org/I17-1075) (Lau et al., IJCNLP 2017)
ACL
- Jey Han Lau, Lianhua Chi, Khoi-Nguyen Tran, and Trevor Cohn. 2017. End-to-end Network for Twitter Geolocation Prediction and Hashing. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 744–753, Taipei, Taiwan. Asian Federation of Natural Language Processing.