Label Embedding using Hierarchical Structure of Labels for Twitter Classification

被引：0

作者：

Miyazaki, Taro ^{[1
]}

Makino, Kiminobu ^{[1
]}

Takei, Yuka ^{[1
]}

Okamoto, Hiroki ^{[1
]}

Goto, Jun ^{[1
]}

机构：

[1] NHK Sci & Technol Res Labs, Tokyo, Japan

来源：

2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019): PROCEEDINGS OF THE CONFERENCE | 2019年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：6317 / 6322

页数：6

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