KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text
Centre for Language Technology（语言技术中心）；Department of Nordic Studies and Linguistics（北欧研究与语言学系）；University of Copenhagen（哥本哈根大学）
英文摘要：
In this paper we present our approach for detecting signs of depression from social media text. Our model relies on word unigrams, part-of-speech tags, readabilitiy measures and the use of first, second or third person and the number of words. Our best model obtained a macro F1-score of 0.439 and ranked 25th, out of 31 teams. We further take advantage of the interpretability of the Logistic Regression model and we make an attempt to interpret the model coefficients with the hope that these will be useful for further research on the topic.
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。
