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        <title>A Hierarchical Location Prediction Neural Network for Twitter User Geolocation</title>
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        <namePart type="given">Binxuan</namePart>
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            <title>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</title>
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            <namePart type="given">Kentaro</namePart>
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        <name type="personal">
            <namePart type="given">Jing</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T A Hierarchical Location Prediction Neural Network for Twitter User Geolocation
%A Huang, Binxuan
%A Carley, Kathleen
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F huang-carley-2019-hierarchical
%X 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.
%R 10.18653/v1/D19-1480
%U https://aclanthology.org/D19-1480/
%U https://doi.org/10.18653/v1/D19-1480
%P 4732-4742

Markdown (Informal)

[A Hierarchical Location Prediction Neural Network for Twitter User Geolocation](https://aclanthology.org/D19-1480/) (Huang & Carley, EMNLP-IJCNLP 2019)

• A Hierarchical Location Prediction Neural Network for Twitter User Geolocation (Huang & Carley, EMNLP-IJCNLP 2019)

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.