Sentence Segmentation and Disfluency Detection in Narrative Transcripts from Neuropsychological Tests

Natural Language Processing (NLP) tools aiming at the diagnosis of language impairing dementias generally extract several textual metrics of narrative transcripts. However, the absence of sentence boundary segmentation in transcripts prevents the direct application of NLP methods which rely on these marks to work properly, such as taggers and parsers. We present a method to segment the transcripts into sentences and another to detect the disfluencies present in them, to serve as a preprocessing step for the application of subsequent NLP tools. Our methods use recurrent convolutional neural networks with prosodic, morphosyntactic features, and word embeddings. We evaluated both tasks intrinsically, analyzing the most important features, comparing the proposed methods to simpler ones, and identifying the main hits and misses. In addition, a final method was created to combine all tasks and it was evaluated extrinsically using 9 syntactic metrics of Coh-Metrix-Dementia. In the intrinsic evaluations, we showed that our method achieved (i) state-of-the-art results for the sentence segmentation task on impaired speech, and (ii) results that are similar to related works for the English language for disfluency detection tasks. Regarding the extrinsic evaluation, only 3 metrics showed a statistically significant difference between manual MCI transcripts and those generated by our method, suggesting that our method is capable to preprocess transcriptions to be further analyzed by NLP tools.