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

Abstract

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.