Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks
Table 3: F1 for boundary class for each feature set on Cinderella and Constitution data using our method.
['[BOLD] Features', '[BOLD] Cinderella  [BOLD] CTL', '[BOLD] Cinderella  [BOLD] CTL', '[BOLD] Cinderella  [BOLD] CTL', '[BOLD] Cinderella  [BOLD] MCI', '[BOLD] Cinderella  [BOLD] MCI', '[BOLD] Cinderella  [BOLD] MCI', '[BOLD] Constitution  [BOLD] L', '[BOLD] Constitution  [BOLD] L', '[BOLD] Constitution  [BOLD] L', '[BOLD] Constitution  [BOLD] S', '[BOLD] Constitution  [BOLD] S', '[BOLD] Constitution  [BOLD] S']
In this subsection, we evaluate the performance of our classifier (RCNN) for the Cinderella and Constitution datasets. This may be related to the text quality, as the Cinderella transcripts presents many disfluences, characteristic of spontaneous speech. As expected, results for CTL were higher than for MCI, since CTL narratives contain less disfluencies. Another important observation is that our method performs much better than the baseline. Where the baseline represents the results for a classifier that predicts all words as B. The Constitution results show us that traditional machine learning techniques used in NLP can be applied to this scenario, since the differences in the Cinderella data are few. When we compare the Constitution size relation we find out that corpus size is not greatly affected by the results, since the results for Constitution S were slightly better than for Constitution L. We think that, even with less data, our method performs better on Constitution S because of the distribution of sentence quantity in the dataset, where Constitution S has an average of 23.48 sentences per text, while Constitution L has an average of only 7.56 sentences per text.