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

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 315–325, Valencia, Spain, April 3-7, 2017. c⃝2017 Association for Computational Linguistics Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks Marcos Vin´ıcius Treviso Christopher Shulby Sandra Maria Alu´ısio marcostreviso@usp.br cshulby@icmc.usp.br sandra@icmc.usp.br Interinstitutional Center for Computational Linguistics (NILC) Institute of Mathematical and Computer Sciences University of S˜ao Paulo Abstract Automated discourse analysis tools based on Natural Language Processing (NLP) aiming at the diagnosis of language- impairing dementias generally extract sev- eral textual metrics of narrative transcripts. However, the absence of sentence bound- ary segmentation in the transcripts pre- vents the direct application of NLP meth- ods which rely on these marks to func- tion properly, such as taggers and parsers. We present the first steps taken towards automatic neuropsychological evaluation based on narrative discourse analysis, pre- senting a new automatic sentence segmen- tation method for impaired speech. Our model uses recurrent convolutional neu- ral 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 re- sults for healthy elderly (CTL) (F1 = 0.74) and Mild Cognitive Impairment (MCI) pa- tients (F1 = 0.70) than the Conditional Random Fields method (F1 = 0.55 and 0.53, respectively) used in the same con- text 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. 1 Introduction Mild Cognitive Impairment (MCI) has recently re- ceived much attention, as it may represent a pre- clinical state of Alzheimer’s disease (AD). MCI can affect one or multiple cognitive domains (e.g. memory, language, visuospatial skills and the ex- ecutive function); the kind that affects memory, called amnestic MCI, is the most frequent and that which most often converts to AD (Janoutov´a et al., 2015). As dementias are chronic progressive dis- eases, it is important to identify them in the early stages, because early detection yields a greater chance of success for non-pharmacological treat- ment strategies such as cognitive training, physical activity and socialization (Teixeira et al., 2012). The definition of MCI diagnostic criteria is con- ducted mainly by the cognitive symptoms pre- sented by patients in standardized tests and by functional impairments in daily life (McKhann et al., 2011). Difficulties related with narrative dis- course deficits (e.g. repetitions or gaps during the narrative) may lead an elderly individual to look for a specialist. Narrative discourse is the re- production of an experienced episode (necessarily evoking memory), respecting temporal and causal relations among events. Although MCI is clini- cally characterized by episodic memory deficits, language impairment may also occur. Certain widely used neuropsychological tests require patients to retell or understand a story. This is the case of the logical memory test, where one reproduces a story after listening to it. The higher the number of recalled …

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