ERTIM@MC2：多样化论证性推文检索
ERTIM@MC2: Diversified Argumentative Tweets Retrieval
AI总结：
本文针对 CLEF MC2 2018 任务 2，提出通过论证复合体衡量推文论证性、结合节日方面覆盖度衡量多样性，并采用聚类选取方法实现多样化论证性推文检索。
摘要：
本文介绍了我们参加 CLEF MC2 2018 版任务 2“挖掘观点论证”的情况。该任务旨在从一个大规模多语言集合中，检测关于某些节日的、最具论证性和多样性的英语和法语推文。我们通过计算一条推文所包含的论证复合体的数量来衡量其论证性。我们将论证复合体视为观点表达与其事实支撑以及特定结构之间的组合。关于多样性，我们考虑推文所覆盖的节日方面的数量。初始步骤对原始数据集进行过滤，以满足任务的语言和主题要求。然后，我们计算并整合语言描述符，以检测推文中的主张及其各自的理由。最后一步通过根据文本内容对推文进行聚类，并从每个聚类中选择最具论证性的推文，来提取最多样化的论证。我们在论文结尾描述了我们所提交的不同运行中组合这些描述符的不同方式，并讨论了它们的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
