Label Embedding using Hierarchical Structure of Labels for Twitter Classification

Taro Miyazaki, Kiminobu Makino, Yuka Takei, Hiroki Okamoto, Jun Goto

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.
Anthology ID:
D19-1660
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:
6317–6322
Language:
URL:
https://aclanthology.org/D19-1660
DOI:
10.18653/v1/D19-1660
Bibkey:
Cite (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.
Cite (Informal):
Label Embedding using Hierarchical Structure of Labels for Twitter Classification (Miyazaki et al., EMNLP-IJCNLP 2019)
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