Abstract
This paper presents the overview of the shared task, emotion detection in code-switching text, in NLPCC 2018. The submitted systems are expected to automatically determine the emotions in the Chinese-English code-switching text. Different from monolingual text, code-switching text contains more than one language, and the emotion can be expressed by either monolingual or bilingual form. Hence, the challenges are: how to integrate both monolingual and bilingual forms to detect emotion, and how to bridge the gap to between two languages. Our shared task has 19 team participants. The highest F-score was 0.515. In this paper, we introduce the task, the corpus, the participating teams, and the evaluation results.
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Acknowledgment
We would like to thank the participants for their valuable feedback and results. We should thank Dr. Sophia Yat Mei Lee and Helena Yan Ping Lau for their excellent works on corpus annotation and analysis. The work is supported by the National Natural Science Foundation of China (61331011, 61751206), and the Early Career Scheme (ECS) sponsored by the Research Grants Council of Hong Kong (No. PolyU 5593/13H).
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Wang, Z., Li, S., Wu, F., Sun, Q., Zhou, G. (2018). Overview of NLPCC 2018 Shared Task 1: Emotion Detection in Code-Switching Text. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_39
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DOI: https://doi.org/10.1007/978-3-319-99501-4_39
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