The acquisition of the clitic ci among typically developing Italian preschoolers: preliminary data

This paper explores the acquisitional patterns of the Italian ci morpheme and its potential role as a clinical marker for Developmental Language Disorder (DLD) in Italian-speaking children, taking into account its distributional, sociolinguistic, and typological properties. To this purpose, we (i) administered a test for the elicited production of clitic pronouns in Italian – which we will refer to as “T-PEC” in the following – to 126 school-aged Italian speakers and (ii) developed a novel test focused on the ci morpheme in order to investigate its production by five-year-old typically developing children. The results and their theoretical interpretations are of particular interest since they could shed light on the acquisition of the morpheme, thus helping understand both typical and atypical grammar development. Given the continuity of the two, it could also be applied to the diagnosis and rehabilitation of DLD, which remains a major challenge for child neuropsychiatrists, psychologists, and speech-language therapists.This paper explores the acquisitional patterns of the Italian ci morpheme and its potential role as a clinical marker for Developmental Language Disorder (DLD) in Italian-speaking children, taking into account its distributional, sociolinguistic, and typological properties. To this purpose, we (i) administered a test for the elicited production of clitic pronouns in Italian – which we will refer to as “T-PEC” in the following – to 126 school-aged Italian speakers and (ii) developed a novel test focused on the ci morpheme in order to investigate its production by five-year-old typically developing children. The results and their theoretical interpretations are of particular interest since they could shed light on the acquisition of the morpheme, thus helping understand both typical and atypical grammar development. Given the continuity of the two, it could also be applied to the diagnosis and rehabilitation of DLD, which remains a major challenge for child neuropsychiatrists, psychologists, and speech-language therapists

The Automatic Extraction of Linguistic Biomarkers as a Viable Solution for the Early Diagnosis of Mental Disorders

Digital Linguistic Biomarkers extracted from spontaneous language productions proved to be very useful for the early detection of various mental disorders. This paper presents a computational pipeline for the automatic processing of oral and written texts: the tool enables the computation of a rich set of linguistic features at the acoustic, rhythmic, lexical, and morphosyntactic levels. Several applications of the instrument - for the detection of Mild Cognitive Impairments, Anorexia Nervosa, and Developmental Language Disorders - are also briefly discussed

Extraction and Classification of Acoustic Features from Italian Speaking Children with Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) are a group of complex developmental conditions whose effects and severity show high intraindividual variability. However, one of the main symptoms shared along the spectrum is social interaction impairments that can be explored through acoustic analysis of speech production. In this paper, we compare 14 Italian-speaking children with ASD and 14 typically developing peers. Accordingly, we extracted and selected the acoustic features related to prosody, quality of voice, loudness, and spectral distribution using the parameter set eGeMAPS provided by the openSMILE feature extraction toolkit. We implemented four supervised machine learning methods to evaluate the extraction performances. Our findings show that Decision Trees (DTs) and Support Vector Machines (SVMs) are the best-performing methods. The overall DT models reach a 100% recall on all the trials, meaning they correctly recognise autistic features. However, half of its models overfit, while SVMs are more consistent. One of the results of the work is the creation of a speech pipeline to extract Italian speech biomarkers typical of ASD by comparing our results with studies based on other languages. A better understanding of this topic can support clinicians in diagnosing the disorder

Analisi linguistica dei testi di un outsider. Note di metodo

Il contributo illustra la metodologia adottata per l'analisi linguistica qualitativa e quantitativa degli écrits bruts di R

Inter-Annotator Agreement in linguistica: una rassegna critica

I coefficienti di Inter-Annotator Agreement sono ampiamente utilizzati in Linguistica Computazionale e NLP per valutare il livello di “affidabilità” delle annotazioni linguistiche. L’articolo propone una breve revisione della letteratura scientifica sull’argomento.Agreement indexes are widely used in Computational Linguistics and NLP to assess the reliability of annotation tasks. The paper aims at reviewing the literature on the topic, illustrating chance-corrected coefficients and their interpretation

“ODIO TUTTO CIÒ, VOGLIO LE OSSA”: UNA PRIMA INDAGINE SULLE CARATTERISTICHE LINGUISTICHE DELLE PAGINE SOCIAL PRO-ANA IN LINGUA ITALIANA

Questo articolo presenta il primo profilo linguistico dell’Anoressia Nervosa (AN) per la lingua italiana a partire dall’analisi di pagine web pro-ana (cioè, resoconti che promuovono comportamenti alimentari potenzialmente pericolosi per la vita come la fame, il vomito autoindotto e l’abuso di lassativi). L’analisi si concentra sulle caratteristiche lessicali dei nomi utente e delle biografie, sull’uso di metafore concretizzate e sulla selezione dei deittici personali e dei morfemi di tempo nei testi. I risultati proposti mirano a far luce sulla fattibilità di trasformare le intuizioni linguistiche in uno strumento di screening computazionale su larga scala.   “I hate this, i want bones”: an initial survey of the linguistic characteristics of Italian-language pro-ana social pages This paper presents the first linguistic profile of Anorexia Nervosa (AN) for the Italian language starting from the analysis of pro-ana web pages (i.e., accounts promoting potentially life-threatening eating behaviors as life-choices such as starvation, self-induced vomiting and laxative abuse). The analysis focuses on the lexical features of usernames and bios, the usage of concretized metaphors and the selection of both personal deictics and tense morphemes in the texts. The proposed findings aim to shed light on the feasibility of turning linguistic insights into a large-scale computational screening tool

Trascrivere il parlato patologico

Il saggio, pubblicato nell'Appendice "Trascrivere le risorse orali" del volume monografico "Vademecum per il trattamento delle fonti orali" (serie: Quaderni della Rassegna degli Archivi di Stato, n.114), presenta le principali questioni associate alla trascrizione fonetica ed ortofgrafica del parlato "patologico"

Digital Linguistic Biomarkers: Beyond Paper and Pencil Tests

noneRecent research has demonstrated that automatically obtainable and analysed quantitative linguistic data, extractable from a person’s verbal productions, can be useful for identifying and classifying individuals with cognitive impairments, at an early stage. Subtle language deficits can be employed as “digital linguistic biomarkers”, namely objective, quantifiable behavioral data which can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Classical pen-and-paper neuropsychological tests are costly and time consuming to process, imposing limitations since manually captured features and results can be prone to human error and bias. This Research Topic aims at bringing together research on digital linguistic biomarkers from different cognitive science subfields. We welcome original research or systematic reviews on the use of Natural Language Processing (NLP) methods and tools for e.g. clinical diagnosis, evaluation of disease severity, and prognosis.openGloria Gagliardi; Dimitrios Kokkinakis; Jon Andoni DunabeitiaGloria Gagliardi; Dimitrios Kokkinakis; Jon Andoni Dunabeiti

Speech Analysis by Natural Language Processing Techniques: A Possible Tool for Very Early Detection of Cognitive Decline?

Background: The discovery of early, non-invasive biomarkers for the identification of “preclinical” or “pre-symptomatic” Alzheimer's disease and other dementias is a key issue in the field, especially for research purposes, the design of preventive clinical trials, and drafting population-based health care policies. Complex behaviors are natural candidates for this. In particular, recent studies have suggested that speech alterations might be one of the earliest signs of cognitive decline, frequently noticeable years before other cognitive deficits become apparent. Traditional neuropsychological language tests provide ambiguous results in this context. In contrast, the analysis of spoken language productions by Natural Language Processing (NLP) techniques can pinpoint language modifications in potential patients. This interdisciplinary study aimed at using NLP to identify early linguistic signs of cognitive decline in a population of elderly individuals.Methods: We enrolled 96 participants (age range 50–75): 48 healthy controls (CG) and 48 cognitively impaired participants: 16 participants with single domain amnestic Mild Cognitive Impairment (aMCI), 16 with multiple domain MCI (mdMCI) and 16 with early Dementia (eD). Each subject underwent a brief neuropsychological screening composed by MMSE, MoCA, GPCog, CDT, and verbal fluency (phonemic and semantic). The spontaneous speech during three tasks (describing a complex picture, a typical working day and recalling a last remembered dream) was then recorded, transcribed and annotated at various linguistic levels. A multidimensional parameter computation was performed by a quantitative analysis of spoken texts, computing rhythmic, acoustic, lexical, morpho-syntactic, and syntactic features.Results: Neuropsychological tests showed significant differences between controls and mdMCI, and between controls and eD participants; GPCog, MoCA, PF, and SF also discriminated between controls and aMCI. In the linguistic experiments, a number of features regarding lexical, acoustic and syntactic aspects were significant in differentiating between mdMCI, eD, and CG (non-parametric statistical analysis). Some features, mainly in the acoustic domain also discriminated between CG and aMCI.Conclusions: Linguistic features of spontaneous speech transcribed and analyzed by NLP techniques show significant differences between controls and pathological states (not only eD but also MCI) and seems to be a promising approach for the identification of preclinical stages of dementia. Long duration follow-up studies are needed to confirm this assumption