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Wachsmuth, H., Al-Khatib, K., & Stein, B. (2016). Using Argument Mining to Assess the Argumentation Quality of Essays. In Y. Matsumoto, & R. Prasad (Eds.), Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers (pp. 1680-1691). The COLING 2016 Organizing Committee.

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• TITLE: Research on the Construction of Chinese Argument Corpus

  AUTHORS: Chen Zhang

  KEYWORDS: Argumentation Mining, Corpus, Emotion Classification

  JOURNAL NAME: Open Journal of Modern Linguistics, Vol.12 No.1, February 25, 2022

  ABSTRACT: Argument mining is an important task of natural language processing. As a branch of sentiment computing, its task is to automatically extract arguments from unstructured text documents in order to provide structured data for machine learning and deep learning models. It has recently become a hot topic because of its potential to process information from the Internet in an innovative way, especially its ability to process information from social media. Faced with the serious shortage of annotated corpora used to train supervised learning algorithms in the field of argumentation mining, we created a reliable annotated corpus of Chinese argument structures. In addition to the argument-oriented mining task, the close relationship between the direction of sentiment calculation and its consideration is also considered. At the same time, the data set can also be used as a sentiment classification task.