Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy
Table 6: F1 score of the models trained with embeddings with dimensionality of 300 and 100 respectively.
['[EMPTY]', 'Freq. Num.', 'SVM-BOW', '100d CNN-Glove', '100d GRU-Skip-thought', '300d CNN-Glove', '300d GRU-Skip-thought']
We only include the results of the best performing models, SVMs, CNNs and GRUs, due to limited space. The results show that both models outperform SVM-BOW in larger embedding dimensions. Although SVM-BOW is comparable to 100 dimensional GRU-Skip-thought in terms on average F1, in all other cases CNN-GloVe and GRU-Skip-thought overshadow SVM-BOW. We also find that CNN-GloVe on average works better than GRU-Skip-thought, which is expected as the space of words is smaller in comparison to the space of sentences so the word vectors can be more accurately trained. While the CNN operating on 100 dimensional word vectors is comparable to the CNN operating on 300 dimensional word vectors, the GRU-Skip-thought tends to be worse on 100 dimensional skip-thoughts, suggesting that sentence vectors generally need to be of a higher dimension to represent the meaning more accurately than word vectors.