@inproceedings{zhang-etal-2016-user,
title = "User Classification with Multiple Textual Perspectives",
author = "Zhang, Dong and
Li, Shoushan and
Wang, Hongling and
Zhou, Guodong",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1199",
pages = "2112--2121",
abstract = "Textual information is of critical importance for automatic user classification in social media. However, most previous studies model textual features in a single perspective while the text in a user homepage typically possesses different styles of text, such as original message and comment from others. In this paper, we propose a novel approach, namely ensemble LSTM, to user classification by incorporating multiple textual perspectives. Specifically, our approach first learns a LSTM representation with a LSTM recurrent neural network and then presents a joint learning method to integrating all naturally-divided textual perspectives. Empirical studies on two basic user classification tasks, i.e., gender classification and age classification, demonstrate the effectiveness of the proposed approach to user classification with multiple textual perspectives.",
}
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%0 Conference Proceedings
%T User Classification with Multiple Textual Perspectives
%A Zhang, Dong
%A Li, Shoushan
%A Wang, Hongling
%A Zhou, Guodong
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F zhang-etal-2016-user
%X Textual information is of critical importance for automatic user classification in social media. However, most previous studies model textual features in a single perspective while the text in a user homepage typically possesses different styles of text, such as original message and comment from others. In this paper, we propose a novel approach, namely ensemble LSTM, to user classification by incorporating multiple textual perspectives. Specifically, our approach first learns a LSTM representation with a LSTM recurrent neural network and then presents a joint learning method to integrating all naturally-divided textual perspectives. Empirical studies on two basic user classification tasks, i.e., gender classification and age classification, demonstrate the effectiveness of the proposed approach to user classification with multiple textual perspectives.
%U https://aclanthology.org/C16-1199
%P 2112-2121
Markdown (Informal)
[User Classification with Multiple Textual Perspectives](https://aclanthology.org/C16-1199) (Zhang et al., COLING 2016)
ACL
- Dong Zhang, Shoushan Li, Hongling Wang, and Guodong Zhou. 2016. User Classification with Multiple Textual Perspectives. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2112–2121, Osaka, Japan. The COLING 2016 Organizing Committee.