Label Embedding using Hierarchical Structure of Labels for Twitter Classification

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 6317–6322, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 6317 Label Embedding using Hierarchical Structure of Labels for Twitter Classification Taro Miyazaki Kiminobu Makino Yuka Takei Hiroki Okamoto Jun Goto NHK Science and Technology Research Laboratories {miyazaki.t-jw, makino.k-gg, takei.y-ek, okamoto.h-iw, goto.j-fw} @nhk.or.jp Abstract Twitter is used for various applications such as disaster monitoring and news material gather- ing. In these applications, each Tweet is clas- sified into pre-defined classes. These classes have a semantic relationship with each other and can be classified into a hierarchical struc- ture, which is regarded as important informa- tion. Label texts of pre-defined classes them- selves also include important clues for clas- sification. Therefore, we propose a method that can consider the hierarchical structure of labels and label texts themselves. We con- ducted evaluation over the Text REtrieval Con- ference (TREC) 2018 Incident Streams (IS) track dataset, and we found that our method outperformed the methods of the conference participants. 1 Introduction Twitter is used for various applications such as disaster monitoring (Ashktorab et al., 2014; Mizuno et al., 2016) and news material gathering (Vosecky et al., 2013; Hayashi et al., 2015). In these applications, each Tweet is classified into pre-defined classes. These classes have a seman- tic relationship with each other and can be classi- fied into a hierarchical structure. Consider a news material gathering system that has classes such as “Tornado,” “Flood,” and “Riot.” These can be classified into two upper classes, “Natural disas- ter” (“Tornado” and “Flood” as lower classes) and “Incident” (“Riot” as a lower class). These rela- tionships can be an important clue for classifica- tion. Needless to say, label texts of pre-defined classes themselves include important clues. Peo- ple can understand what the criterion of the clas- sification is by reading labels. Therefore, we pro- pose a method that can consider the hierarchical structure of labels and the labels themselves. Our method is based on the Label Embedding (LE) method (Zhang et al., 2018). The typical LE method embeds input text and label text, and then the embedded vectors are fed into “Matcher,” which outputs the score between the input text and label. We use a two-step attention mechanism for LE to consider the hierarchical structure of la- bels. We confirm the effectiveness of our method through evaluation over the Text REtrieval Con- ference (TREC) 2018 Incident Streams (IS) track dataset. Our contributions are as follows: (1) we pro- pose label embedding using the hierarchical struc- ture of labels, and (2) our method outperformed others on the TREC 2018 IS track dataset for several metrics, including the Any-type Micro F1 score, which is the target metric of the track. 2 TREC 2018 IS track The TREC 2018 IS track is a shared-task that aims to mature social media-based emergency re- sponse technology (McCreadie et al., 2019). The task of the track is classifying …

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