论文标题
kucst@lt-edi-acl2022：从社交媒体文本中检测抑郁症的迹象
KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text
论文作者
论文摘要
在本文中，我们介绍了我们从社交媒体文本中检测抑郁症状的方法。我们的模型依赖于单词umigrams，言论的部分，重新计算措施以及第一，第二或第三人称的使用以及单词数量。我们的最佳模型获得了0.439的宏F1得分，在31支球队中排名第25位。我们进一步利用了逻辑回归模型的可解释性，并试图解释模型系数，希望这些系数对该主题的进一步研究有用。
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.
