This paper describes our efforts in predicting current and future psychological health from childhood essays within the scope of the CLPsych-2018 Shared Task. We experimented with a number of different models, including recurrent and convolutional networks, Poisson regression, support vector regression, and L1 and L2 regularized linear regression. We obtained the best results on the training/development data with L2 regularized linear regression (ridge regression) which also got the best scores on main metrics in the official testing for task A (predicting psychological health from essays written at the age of 11 years) and task B (predicting later psychological health from essays written at the age of 11).

翻译：本文介绍我们在CLPsych-2018共同任务范围内从童年论文中预测当前和未来心理健康的努力,我们试验了若干不同的模型,包括经常性和革命性网络、Poisson回归、支持矢量回归以及L1和L2常规线性回归。我们用L2常规线性回归(山脊回归)在培训/发展数据方面取得了最佳结果,这也在任务A(11岁撰写的论文预示心理健康)和任务B(11岁撰写的论文预示后来的心理健康)的正式测试中,在主要指标方面得分最高。