Tübingen-Oslo system: Linear regression works the best at Predicting Current and Future Psychological Health from Childhood Essays in the CLPsych 2018 Shared Task
This work addresses psychological health prediction from text for clinical or research applications, but it is incremental as it applies standard methods to a specific dataset.
The paper tackled predicting current and future psychological health from childhood essays, finding that L2 regularized linear regression (ridge regression) achieved the best results on both tasks in the CLPsych 2018 Shared Task.
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).