Interpreting a Convolutional Text Classification Neural Network on a Clinical Dataset

Andreas Pung
In this Bachelor’s Thesis, a convolutional text classification neural network is interpreted to find out why the neural network makes such predictions. To perform the analysis, the clinical DementiaBank dataset was used in which people with Alzheimer’s disease describe the Boston cookie theft image. The task of the binary classification was to identify based on the given text whether a person has Alzheimer’s or not. Interpretation methods described in Jacovi et al. (2018) were implemented. In addition to that, concrete examples of texts are interpreted in this thesis. Out of all the analyses performed, informative and uninformative ngrams and slot activation vectors with their clustering yield good results. Negative ngrams analysis results were substandard because of the specificity of the dataset.
Graduation Thesis language
Graduation Thesis type
Bachelor - Computer Science
Kairit Sirts
Defence year