Automatic assessment of connected speech and its relation to memory in Alzheimer's Disease in low education.

INTRODUCTION: Discourse is one of the main linguistic aspects affected by Alzheimer's Disease (AD) and its relationship with memory needs to be further studied, mainly in low education and low socioeconomic status (SES) groups. The present study aimed at investigating differences in the recall of short narratives between participants with mild AD (AD) and a control group of typical older adults (CG) with the use of automatic assessment. METHODS: 17 older adults diagnosed with AD (mean age 76.41, mean education 5,82) and 34 typical older adults (mean age 74.26, mean education 7.09) were asked to listen to and then retell a short story. Syntactic, lexical, and semantic features were assessed via the NILC-Metrix software, and the features were correlated with episodic, working, and semantic memory assessment. RESULTS: Differences were found in 7 of the 34 features assessed. Syntactically, the group diagnosed with AD produced narratives with fewer sentences, fewer words per sentence, and lower Yngve depth scores. Lexically, the AD group produced narratives with fewer words and prepositions per sentence. Semantically, the narratives produced by the AD group featured words with a lower mean age of acquisition, and lower Brunét's index scores. For the CG group, episodic memory performance correlated with the ratio of conjunctions. No other significant correlation was found for semantic and working memory in the CG. No correlation was found between memory performance and linguistic features for the AD group. DISCUSSION: The automatic assessment of linguistic features showed impaired narrative recall in participants diagnosed with AD relative to healthy controls at the syntactic, lexical, and semantic levels of discourse. These findings corroborate previous literature showing a decline in discourse production performance resulting from cognitive impairment in AD. CONCLUSION: The assessment of linguistic performance through a narrative recall task provides valuable insights into cognitive decline related to Alzheimer's disease.

RastrOS Project: Natural Language Processing contributions to the development of an eye-tracking corpus with predictability norms for Brazilian Portuguese.

This article presents RastrOS, a new eye-tracking corpus of eye movement data from university students during silent reading of paragraphs of texts in Brazilian Portuguese (BP). The article shows the potential of the corpus for natural language processing (NLP) using it to evaluate the sentence complexity prediction task in BP and it also focuses on the description of NLP resources and methods developed to create the corpus. Specifically, we present: (i) the method used to select the corpus paragraphs from large corpora, using linguistic metrics and clustering algorithms; (ii) the platform for collecting the Cloze test, which is also responsible for creating the project datasets, and (iii) the hybrid semantic similarity method, based on word embedding models and contextualised word representations, used to generate semantic predictability norms. RastrOS can be downloaded from the open science framework repository with the computational infrastructure mentioned above. Datasets with predictability norms of 393 participants and eye-tracking data of 37 participants are available in the OSF repository for this work (https://osf.io/9jxg3/).