  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).
