In this paper, we present our participation to CLEF MC2 2018 edition for the task 2 Mining opinion argumentation. It consists in detecting the most argumentative and diverse Tweets about some festivals in English and French from a massive multilingual collection. We measure argumentativity of a Tweet computing the amount of argumentation compounds it contains. We consider argumentation compounds as a combination between opinion expression and its support with facts and a particular structuration. Regarding diversity, we consider the amount of festival aspects covered by Tweets. An initial step filters the original dataset to fit the language and topic requirements of the task. Then, we compute and integrate linguistic descriptors to detect claims and their respective justifications in Tweets. The final step extracts the most diverse arguments by clustering Tweets according to their textual content and selecting the most argumentative ones from each cluster. We conclude the paper describing the different ways we combined the descriptors among the different runs we submitted and discussing their results.

翻译：在本文中，我们介绍了我们参加了CLEF MC2 2018任务2“挖掘观点争论”中的参赛作品，旨在从大型多语言收集中检测关于一些节日的最具争议和多样化的英语和法语推文。我们通过计算推文中包含的争论化化合物的数量来衡量推文的争论性。我们认为，争论化化合物是观点表达和其根据事实的支持之间的组合，并具有特定的结构。关于多样性，我们考虑由推文涵盖的节日方面数量。初始步骤过滤原始数据集以适应任务的语言和主题要求。然后，我们计算并集成语言描述符以检测推文中的主张及其相应的理由。最后一步按照其文本内容将推文聚类，从每个聚类中选择最具争议性的推文。我们最后总结了我们在提交的不同运行中如何组合描述符以及讨论它们的结果。