Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks

TL;DR

This paper introduces a novel neural network-based method for automatic sentence segmentation in impaired speech transcripts, facilitating NLP analysis for neuropsychological assessment of dementia.

Contribution

It presents the first recurrent convolutional neural network model using prosodic, PoS, and word features for segmenting impaired speech in neuropsychological contexts.

Findings

Outperforms CRF method in F1 score for impaired speech

Robust segmentation for both healthy and MCI speech

Enables automated discourse analysis for dementia diagnosis

Abstract

Automated discourse analysis tools based on Natural Language Processing (NLP) aiming at the diagnosis of language-impairing dementias generally extract several textual metrics of narrative transcripts. However, the absence of sentence boundary segmentation in the transcripts prevents the direct application of NLP methods which rely on these marks to function properly, such as taggers and parsers. We present the first steps taken towards automatic neuropsychological evaluation based on narrative discourse analysis, presenting a new automatic sentence segmentation method for impaired speech. Our model uses recurrent convolutional neural networks with prosodic, Part of Speech (PoS) features, and word embeddings. It was evaluated intrinsically on impaired, spontaneous speech, as well as, normal, prepared speech, and presents better results for healthy elderly (CTL) (F1 = 0.74) and Mild Cognitive Impairment (MCI) patients (F1 = 0.70) than the Conditional Random Fields method (F1 = 0.55 and 0.53, respectively) used in the same context of our study. The results suggest that our model is robust for impaired speech and can be used in automated discourse analysis tools to differentiate narratives produced by MCI and CTL.