利用用户频率信息从社交媒体文本中挖掘区域主义
Exploiting user-frequency information for mining regionalisms from Social Media texts
摘要：
检测区域主义（在某些区域使用的表达方式或词语）的任务传统上依赖于使用问卷和调查，也严重依赖于调查员的专业知识和直觉。社交媒体及其微博服务的涌入产生了前所未有的丰富内容，主要是用户生成的非正式文本，为语言学家扩展语言变异研究提供了新的机会。以前的工作自动检测区域主义主要依赖于词频。在这项工作中，我们提出了一种新的度量的基础上信息论，结合用户频率。我们在阿根廷西班牙语推文语料库上以两种方式测试了这一指标：通过手动注释检索到的术语的相关性，以及作为用户地理定位的特征选择方法。在任何一种情况下，我们的指标都优于其他仅基于词频的技术，这表明测量产生单词的用户数量是有用的。这个工具帮助词典编纂者发现了阿根廷西班牙语的几个未注册单词，以及分配给注册单词的不同含义。
英文摘要：
The task of detecting regionalisms (expressions or words used in certain regions) has traditionally relied on the use of questionnaires and surveys, and has also heavily depended on the expertise and intuition of the surveyor. The irruption of Social Media and its microblogging services has produced an unprecedented wealth of content, mainly informal text generated by users, opening new opportunities for linguists to extend their studies of language variation. Previous work on automatic detection of regionalisms depended mostly on word frequencies. In this work, we present a novel metric based on Information Theory that incorporates user frequency. We tested this metric on a corpus of Argentinian Spanish tweets in two ways: via manual annotation of the relevance of the retrieved terms, and also as a feature selection method for geolocation of users. In either case, our metric outperformed other techniques based solely in word frequency, suggesting that measuring the amount of users that produce a word is informative. This tool has helped lexicographers discover several unregistered words of Argentinian Spanish, as well as different meanings assigned to registered words.
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。
