@inproceedings{miyazaki-etal-2019-label,
title = "Label Embedding using Hierarchical Structure of Labels for {T}witter Classification",
author = "Miyazaki, Taro and
Makino, Kiminobu and
Takei, Yuka and
Okamoto, Hiroki and
Goto, Jun",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1660",
doi = "10.18653/v1/D19-1660",
pages = "6317--6322",
abstract = "Twitter is used for various applications such as disaster monitoring and news material gathering. In these applications, each Tweet is classified into pre-defined classes. These classes have a semantic relationship with each other and can be classified into a hierarchical structure, which is regarded as important information. Label texts of pre-defined classes themselves also include important clues for classification. Therefore, we propose a method that can consider the hierarchical structure of labels and label texts themselves. We conducted evaluation over the Text REtrieval Conference (TREC) 2018 Incident Streams (IS) track dataset, and we found that our method outperformed the methods of the conference participants.",
}
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<abstract>Twitter is used for various applications such as disaster monitoring and news material gathering. In these applications, each Tweet is classified into pre-defined classes. These classes have a semantic relationship with each other and can be classified into a hierarchical structure, which is regarded as important information. Label texts of pre-defined classes themselves also include important clues for classification. Therefore, we propose a method that can consider the hierarchical structure of labels and label texts themselves. We conducted evaluation over the Text REtrieval Conference (TREC) 2018 Incident Streams (IS) track dataset, and we found that our method outperformed the methods of the conference participants.</abstract>
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%0 Conference Proceedings
%T Label Embedding using Hierarchical Structure of Labels for Twitter Classification
%A Miyazaki, Taro
%A Makino, Kiminobu
%A Takei, Yuka
%A Okamoto, Hiroki
%A Goto, Jun
%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 miyazaki-etal-2019-label
%X Twitter is used for various applications such as disaster monitoring and news material gathering. In these applications, each Tweet is classified into pre-defined classes. These classes have a semantic relationship with each other and can be classified into a hierarchical structure, which is regarded as important information. Label texts of pre-defined classes themselves also include important clues for classification. Therefore, we propose a method that can consider the hierarchical structure of labels and label texts themselves. We conducted evaluation over the Text REtrieval Conference (TREC) 2018 Incident Streams (IS) track dataset, and we found that our method outperformed the methods of the conference participants.
%R 10.18653/v1/D19-1660
%U https://aclanthology.org/D19-1660
%U https://doi.org/10.18653/v1/D19-1660
%P 6317-6322
Markdown (Informal)
[Label Embedding using Hierarchical Structure of Labels for Twitter Classification](https://aclanthology.org/D19-1660) (Miyazaki et al., EMNLP-IJCNLP 2019)
ACL
- Taro Miyazaki, Kiminobu Makino, Yuka Takei, Hiroki Okamoto, and Jun Goto. 2019. Label Embedding using Hierarchical Structure of Labels for Twitter Classification. 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 6317–6322, Hong Kong, China. Association for Computational Linguistics.