Ensemble of Binary Classification for the Emotion Detection in Code-Switching Text

  • Xinghua ZhangEmail author
  • Chunyue ZhangEmail author
  • Huaxing ShiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11109)


This paper describes the methods for the DeepIntell who participated the task1 in the NLPCC2018. The task1 is to label the emotion in a code-switching text. Note that, there may be more than one emotion in a post in this task. Hence, the assessment task is a multi-label classification task. At the same time, the post contains more than one language, and the emotion can be expressed by either monolingual or bilingual form. In this paper, we propose a novel method of converting multi-label classification into binary classification task and ensemble learning for code-switching text with sampling and emotion lexicon. Experiments show that the proposed method has achieved better performance in the code-switching text task.


Multi-label classification Binary classification Sampling Emotion lexicon Ensemble learning 


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Harbin Institute of TechnologyHarbinChina
  2. 2.DeepIntell Co. Ltd.HarbinChina

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