Understanding children with language problems. Shula Chiat. Cambridge: Cambridge University Press, 2000. Pp. 286.

2001 ◽  
Vol 22 (4) ◽  
pp. 647-650
Author(s):  
Stacy Silverman
Keyword(s):  
Language Processing ◽  
Language Disorder ◽  
Processing System ◽  
Language Disorders ◽  
Phonological Development ◽  
Language Abilities ◽  
Senior Lecturer ◽  
Problem Solve ◽  
The Impact

Examining the language abilities of children with language disorders should be a deductive process, using much more than the data that formal measures provide. The assessment should be a systematic, psycholinguistic exploration of aspects of a child's input and output, with a focus on the attempt to pinpoint specific areas of deficit within the language-processing system. Chiat, in this insightful and extremely accessible book, provides basic profiles of children with language disorders, along with case study examples, that both illustrate various forms of language disorder and demonstrate the use of tasks, commonly applied in psycholinguistic research, to problem-solve specific cases. Chiat is a senior lecturer in Linguistics at City University, London, where she is an established researcher who focuses primarily on phonological development and disorders and the impact of impaired phonology on lexical/semantic development.

Making the most of MOGUL: Reflections on interlanguage in childhood language disorders

2017 ◽  
Vol 55 (4) ◽  
pp. 349-364
Author(s):  
Susan H. Foster-Cohen
Keyword(s):  
Language Processing ◽  
Language Change ◽  
Language Disorder ◽  
Language Model ◽  
Language Disorders ◽  
Relevance Theory ◽  
Psychological Resources ◽  
Wide Range ◽  
On Line ◽  
The Impact

Abstract Interlanguage is a concept that is manifest in any trajectory of language change in a learner: typical first language, second language or language disorder. To understand those trajectories we need a rich psychological model of what creates them. This paper applies one such model–Sharwood Smith and Truscott’s Modular On-Line Growth and Use of Language model (MOGUL)–to childhood developmental language disorders, and suggests that the model’s components of language processing and their interaction shed significant light on why children with a wide range of different language disorders exhibit the language behaviours that are the characteristic of their diagnoses. Following a brief summary of the MOGUL model, the paper explores the impact on language development of differences in sensori-motor input, in the functioning of the various modules and the interfaces between them, and in the storage and activation of memory. Like Relevance Theory, with which the paper makes a direct connection, MOGUL encourages one to take a view of developmental language disorder as emerging from the same set of psychological resources as typical development (first or second) but as being the result of adjustments to, or compensations for, differences in how the various modules function and connect with each other.

Using Machine Learning to Capture Quality Metrics from Natural Language: A Case Study of Diabetic Eye Exams

2021 ◽  
Author(s):  
Allan Fong ◽  
Nicholas Scoulios ◽  
H. Joseph Blumenthal ◽  
Ryan E. Anderson
Keyword(s):  
Logistic Regression ◽  
Natural Language ◽  
Language Processing ◽  
Synthesis Process ◽  
Support Vector ◽  
Free Text ◽  
Svm Model ◽  
Eye Exams ◽  
The Impact

Abstract Background and Objective The prevalence of value-based payment models has led to an increased use of the electronic health record to capture quality measures, necessitating additional documentation requirements for providers. Methods This case study uses text mining and natural language processing techniques to identify the timely completion of diabetic eye exams (DEEs) from 26,203 unique clinician notes for reporting as an electronic clinical quality measure (eCQM). Logistic regression and support vector machine (SVM) using unbalanced and balanced datasets, using the synthetic minority over-sampling technique (SMOTE) algorithm, were evaluated on precision, recall, sensitivity, and f1-score for classifying records positive for DEE. We then integrate a high precision DEE model to evaluate free-text clinical narratives from our clinical EHR system. Results Logistic regression and SVM models had comparable f1-score and specificity metrics with models trained and validated with no oversampling favoring precision over recall. SVM with and without oversampling resulted in the best precision, 0.96, and recall, 0.85, respectively. These two SVM models were applied to the unannotated 31,585 text segments representing 24,823 unique records and 13,714 unique patients. The number of records classified as positive for DEE using the SVM models ranged from 667 to 8,935 (2.7–36% out of 24,823, respectively). Unique patients classified as positive for DEE ranged from 3.5 to 41.8% highlighting the potential utility of these models. Discussion We believe the impact of oversampling on SVM model performance to be caused by the potential of overfitting of the SVM SMOTE model on the synthesized data and the data synthesis process. However, the specificities of SVM with and without SMOTE were comparable, suggesting both models were confident in their negative predictions. By prioritizing to implement the SVM model with higher precision over sensitivity or recall in the categorization of DEEs, we can provide a highly reliable pool of results that can be documented through automation, reducing the burden of secondary review. Although the focus of this work was on completed DEEs, this method could be applied to completing other necessary documentation by extracting information from natural language in clinician notes. Conclusion By enabling the capture of data for eCQMs from documentation generated by usual clinical practice, this work represents a case study in how such techniques can be leveraged to drive quality without increasing clinician work.

Nonword Repetition Performance Differentiates Children Who Stutter With and Without Concomitant Speech Sound and Developmental Language Disorders

2022 ◽  
pp. 1-13
Author(s):  
Katelyn L. Gerwin ◽  
Bridget Walsh ◽  
Seth E. Tichenor
Keyword(s):  
Sound Production ◽  
Language Disorder ◽  
Speech Sound ◽  
Language Disorders ◽  
Nonword Repetition ◽  
Driving Factor ◽  
Speech And Language ◽  
Speech Sound Disorder ◽  
Language Abilities ◽  
Speech And Language Disorders

Purpose: The aim of this study was to examine how nonword repetition (NWR) performance may be impacted by the presence of concomitant speech and language disorders in young children who stutter (CWS). Method: One hundred forty-one children (88 CWS and 53 children who do not stutter [CWNS]) participated. CWS were divided into groups based on the presence of speech sound and/or language disorder or typical speech sound production and language abilities. NWR abilities were measured using stimuli composed of one- to four-syllable nonwords. Results: CWS with typical speech and language and CWNS had higher accuracy scores than CWS with concomitant speech and language disorders. We found no difference in accuracy scores between CWNS and CWS with typical speech and language abilities, nor did we find differences between CWS with speech sound disorder and CWS with both speech sound and language disorders. Accuracy decreased as nonword length increased for all groups. Conclusions: We found that the presence of a concomitant speech and language disorder was a driving factor behind poorer NWR performance in CWS. Accuracy scores differentiated CWS with concomitant disorders from CWS with typical speech and language but not CWS with typical speech and language from CWNS. Considering the speech and language abilities of CWS helped clarify poorer NWR performance and enhances generalizability to the population that exists clinically.

scholarly journals An Artificial Intelligence Chatbot for Young People’s Sexual and Reproductive Health in India (SnehAI): Instrumental Case Study

10.2196/29969 ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. e29969
Author(s):  
Hua Wang ◽  
Sneha Gupta ◽  
Arvind Singhal ◽  
Poonam Muttreja ◽  
Sanghamitra Singh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Reproductive Health ◽  
Language Processing ◽  
Sexual And Reproductive Health ◽  
Processing System ◽  
User Engagement ◽  
Social Good ◽  
Instrumental Case Study ◽  
Instrumental Case

Background Leveraging artificial intelligence (AI)–driven apps for health education and promotion can help in the accomplishment of several United Nations sustainable development goals. SnehAI, developed by the Population Foundation of India, is the first Hinglish (Hindi + English) AI chatbot, deliberately designed for social and behavioral changes in India. It provides a private, nonjudgmental, and safe space to spur conversations about taboo topics (such as safe sex and family planning) and offers accurate, relatable, and trustworthy information and resources. Objective This study aims to use the Gibson theory of affordances to examine SnehAI and offer scholarly guidance on how AI chatbots can be used to educate adolescents and young adults, promote sexual and reproductive health, and advocate for the health entitlements of women and girls in India. Methods We adopted an instrumental case study approach that allowed us to explore SnehAI from the perspectives of technology design, program implementation, and user engagement. We also used a mix of qualitative insights and quantitative analytics data to triangulate our findings. Results SnehAI demonstrated strong evidence across fifteen functional affordances: accessibility, multimodality, nonlinearity, compellability, queriosity, editability, visibility, interactivity, customizability, trackability, scalability, glocalizability, inclusivity, connectivity, and actionability. SnehAI also effectively engaged its users, especially young men, with 8.2 million messages exchanged across a 5-month period. Almost half of the incoming user messages were texts of deeply personal questions and concerns about sexual and reproductive health, as well as allied topics. Overall, SnehAI successfully presented itself as a trusted friend and mentor; the curated content was both entertaining and educational, and the natural language processing system worked effectively to personalize the chatbot response and optimize user experience. Conclusions SnehAI represents an innovative, engaging, and educational intervention that enables vulnerable and hard-to-reach population groups to talk and learn about sensitive and important issues. SnehAI is a powerful testimonial of the vital potential that lies in AI technologies for social good.

scholarly journals Relações semânticas em ontologias: estudo de caso do Blood Project | Semantic relations on ontologies: the case study of the Blood Project

2010 ◽  
Vol 6 (2) ◽  
Author(s):  
Maurício Barcellos Almeida ◽  
Lívia M.D.Teixeira ◽  
Kátia Cardoso Coelho ◽  
Renato R. Souza
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Development Process ◽  
Information Science ◽  
Information Organization ◽  
Semantic Relations ◽  
Medical Texts ◽  
Automatic Tool ◽  
The Impact

Resumo Nos últimos anos, a pesquisa em ontologias tem recebido destaque pelas possibilidades que oferece na organização da informação. No desenvolvimento de ontologias, a fase de conceitualização merece atenção especial por sua importância e complexidade. Esse artigo busca investigar possibilidades de melhorias na fase de conceitualização, adotando aportes da Linguística para verificar variações na semântica das relações entre termos. Apresenta-se uma proposta que abrange: i) um esquema linguístico para identificar relações semânticas em um texto; ii) o uso de uma ferramenta automática para extração de termos de textos médicos; iii) uma avaliação realizada por médicos sobre quais relações seriam mais adequadas. Investiga-se a existência de variações significativas na semântica das relações, o impacto dessa variação no desenvolvimento de ontologias e a validade das relações obtidas. Espera-se contribuir através de novas possibilidades na construção de instrumentos de organização da informação, bem como fornecendo alternativas para os profissionais envolvidos.Palavras-chave ontologias, Ciência da Informação, processamento de linguagem natural, relações semânticas, organização da informação.Abstract In the last years, research on onthologies has received much attention because of the possibilities it offers regarding information organization. During the onthology development process, the phase of conceptualization deserves special attention on account of its importance and complexity. This paper investigates possible improvements in the phase of conceptualization, relying on linguistics theories to verify variations of semantic relations among terms. We present a proposal including: i) a linguistic-based schema which identifies semantic relations in texts; ii) the use of an automatic tool which extracts terms from medical texts; iii) an assessment conducted with physicians requesting which relations are more appropriate. We investigate the possibility of significant variations in the semantic of those relations, the impact of such variation on the development of onthologies and the validity of the obtained relations. We hope to offer new possibilities regarding the construction of information organization instruments, as well as providing alternatives to involved professionals.Keywords onthologies, Information Science, natural language processing, semantic relations, information organization

scholarly journals An Artificial Intelligence Chatbot for Young People’s Sexual and Reproductive Health in India (SnehAI): Instrumental Case Study (Preprint)

2021 ◽  
Author(s):  
Hua Wang ◽  
Sneha Gupta ◽  
Arvind Singhal ◽  
Poonam Muttreja ◽  
Sanghamitra Singh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Reproductive Health ◽  
Language Processing ◽  
Sexual And Reproductive Health ◽  
Processing System ◽  
User Engagement ◽  
Social Good ◽  
Instrumental Case Study ◽  
Instrumental Case

BACKGROUND Leveraging artificial intelligence (AI)–driven apps for health education and promotion can help in the accomplishment of several United Nations sustainable development goals. SnehAI, developed by the Population Foundation of India, is the first Hinglish (Hindi + English) AI chatbot, deliberately designed for social and behavioral changes in India. It provides a private, nonjudgmental, and safe space to spur conversations about taboo topics (such as safe sex and family planning) and offers accurate, relatable, and trustworthy information and resources. OBJECTIVE This study aims to use the Gibson theory of affordances to examine SnehAI and offer scholarly guidance on how AI chatbots can be used to educate adolescents and young adults, promote sexual and reproductive health, and advocate for the health entitlements of women and girls in India. METHODS We adopted an instrumental case study approach that allowed us to explore SnehAI from the perspectives of technology design, program implementation, and user engagement. We also used a mix of qualitative insights and quantitative analytics data to triangulate our findings. RESULTS SnehAI demonstrated strong evidence across fifteen functional affordances: accessibility, multimodality, nonlinearity, compellability, queriosity, editability, visibility, interactivity, customizability, trackability, scalability, glocalizability, inclusivity, connectivity, and actionability. SnehAI also effectively engaged its users, especially young men, with 8.2 million messages exchanged across a 5-month period. Almost half of the incoming user messages were texts of deeply personal questions and concerns about sexual and reproductive health, as well as allied topics. Overall, SnehAI successfully presented itself as a trusted friend and mentor; the curated content was both entertaining and educational, and the natural language processing system worked effectively to personalize the chatbot response and optimize user experience. CONCLUSIONS SnehAI represents an innovative, engaging, and educational intervention that enables vulnerable and hard-to-reach population groups to talk and learn about sensitive and important issues. SnehAI is a powerful testimonial of the vital potential that lies in AI technologies for social good. CLINICALTRIAL

scholarly journals A Developmental Disorder of Signed Language Production in a Native Deaf Signer of ASL

Languages ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 40
Author(s):  
David Quinto-Pozos ◽  
Frances Cooley
Keyword(s):  
American Sign Language ◽  
Language Processing ◽  
Language Production ◽  
Language Disorder ◽  
Deaf Children ◽  
Fine Motor ◽  
Motor Deficits ◽  
Case Study Methodology ◽  
Signed Language

Evidence for a Developmental Language Disorder (DLD) could surface with language processing/comprehension, language production, or a combination of both. Whereas, various studies have described cases of DLD in signing deaf children, there exist few detailed examples of deaf children who exhibit production issues in the absence of processing or comprehension challenges or motor deficits. We describe such a situation by detailing a case study of “Gregory”, a deaf native signer of American Sign Language (ASL). We adopt a detailed case-study methodology for obtaining information from Gregory’s family and school, which we combine with linguistic and non-linguistic data that we collected through one-on-one sessions with Gregory. The results provide evidence of persistent issues with language production (in particular, atypical articulation of some phonological aspects of signs), yet typical comprehension skills and unremarkable fine-motor motor skills. We also provide a snapshot of Gregory’s rich linguistic environment, which we speculate, may serve to attenuate his production deficit. The results of this study have implications for the provision of language services for signing deaf children in schools and also for language therapists. We propose that language therapists who are fluent in signed language be trained to work with signing children.

Genetics of language disorders: clinical conditions, phenotypes, and genes

2007 ◽  
pp. 684-700
Author(s):  
Mabel L. Rice ◽  
Filip Smolík
Keyword(s):  
Twin Studies ◽  
Language Disorders ◽  
Language Impairments ◽  
Speech Impairments ◽  
Language Abilities ◽  
Multiple Dimensions ◽  
Scientific Context ◽  
Ancient Times ◽  
Clinical Conditions

Persons who do not acquire language in the expected ways show variation that can be linked or associated with genetic variations. Just as in ancient times, there is great interest in young children's language abilities as a window onto inherited mechanisms. In the case of language impairments, there are multiple dimensions of possible phenotypes that intertwine language symptoms with more general cognitive and sensory symptoms. This article summarises current investigations of the FOXP2 gene, as a case study of the steps involved in relating language disorders and genes, and progresses toward the molecular level. It also brings in recent investigations of the genetics of reading and speech impairments, and possible overlap with language impairments; describes the outcomes of twin studies, and the ways that phenotype definitions interact with genetic interpretations; and looks at “the first psycholinguistic experiment,” reported by Herodotus and updated to the modern scientific context.

scholarly journals The Influence of Birth Country on Selection of Conference Destination - Employing Natural Language Processing

2018 ◽  
Vol 8 (2) ◽  
pp. 92 ◽  
Author(s):  
Nitza Davidovitch ◽  
Eyal Eckhaus
Keyword(s):  
Language Processing ◽  
Faculty Members ◽  
Birth Country ◽  
Academic Faculty ◽  
Research Findings ◽  
Israel Studies ◽  
Former Ussr ◽  
The Impact ◽  
Academic Conferences

This study deals with immigrant scientists integrated in academia in Israel. Studies on the subject indicate the contribution of immigrant scientists to research. The current study focuses on the influence of scientists' birth country on selecting destinations for academic conferences, as well as on the influence of one's native language on the academic output resulting from research conferences. This is a pilot study exploring the effect of academic conferences from the perspective of birth country - comparing Israeli born and USSR born academics - on the motivation of faculty members to attend conferences, the nature of the conferences they select, the differences between Israeli natives and non-natives with regard to how their perceive the influence of conferences and their contribution to their academic work. This research offers a case study, investigating the effect of scholars from two different countries and cultural background, Israel and USSR. This pioneering research provides the grounds for many extensions, studies that will investigate the impact of other countries and their effect on conference selection. The study addresses a case study of a single university in Israel that absorbed many immigrant scientists from the former USSR in the 1990s. Ninety-four academic faculty members from various departments answered the questionnaire, of them 60.9% women and 39.1% men. Faculty members referred to the conference's contribution with regard to their professional development. In addition, the greater inclination of Israeli researchers than researchers born in the USSR to take their family members with them when travelling for conferences was noted. Since the contribution of immigrant scientists was found to be significant and since academic conferences are a significant part of the work of faculty members and receive considerable funds from academic institutions, the research findings might illuminate the choice of conferences by immigrant scientists and their preferences - compared to native born scientists - with the purpose of examining whether conferences are perceived by Israeli born faculty members as productive.

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