Vocabulary Diversity Research Articles

Names of Russian Oven and Its Components in Arkhangelsk Dialects

This article is dedicated to the study of dialectal vocabulary belonging to the thematic group “Russian Oven,” which is included in the second volume of the “Thematic Dictionary of Arkhangelsk Dialects.” This dictionary documents lexicon related to wooden structures and their components. The primary aim of this research is to record the nominations within the thematic group “Russian Oven” and to determine the meanings of the selected words for inclusion in the dictionary. The material for this article and the dictionary comprises dialectal records housed in the archive of the Department of Russian Language and Speech Culture at the Northern (Arctic) Federal University named after M. V. Lomonosov. The study employed a method of comprehensive sampling of words from the department’s archive, along with descriptive and lexicographic methods to clarify word meanings. The research reveals the diversity of dialectal vocabulary that names parts of the Russian oven; the same component may have various designations across different regions of Arkhangelsk Oblast, leading to the formation of synonyms, as well as phonetic, morphological, and derivational variations. A significant derivational cluster consists of words with the root -печ-, which are formed through suffixation (pechka, pechishcha, pechurok, pechurochek, etc.) and prefix-suffixation (opechek, opechye, podpechek, podpechye, pripechek, pripechye, protipechye, etc.).

Narrative reconstruction in deaf and hearing children: A comparative study in the context of Arabic diglossia

This study explores the narrative skills of deaf and hearing children within the context of Arabic diglossia, a linguistic environment characterised by significant differences between spoken dialects and formal written language. Using Stein and Glenn's (1979) and Bruner's (1991) frameworks, the research analyses the narrative constructions of 13 hearing and 13 deaf children in Kuwait. The findings reveal that hearing children, benefiting from consistent exposure to spoken and formal Arabic, produced more coherent and detailed narratives compared to deaf children. Hearing participants also demonstrated greater vocabulary diversity. Age-related improvements in narrative skills were more pronounced among hearing children, while the impact of sign language exposure on narrative abilities was significant among deaf children. The study underscores the critical role of early language exposure and educational support in fostering narrative development, particularly in a diglossic context. These findings highlight the need for specialised educational strategies to support the unique narrative development needs of deaf children.

Estimates of speech efficiency in monolingual and bilingual speakers of English.

Introduction The Speech Efficiency Score (SES) serves as an acoustic metric for assessing fluency in conversational speech within the temporal domain. This study leverages SES to investigate conversational speech efficiency among native speakers of American English (AE) compared to speakers of Mandarin-accented English (MAE). Methods SES, speaking rate, articulation rate, and vocabulary diversity were measured and compared between two groups: native AE speakers and MAE speakers. The study utilized conversational speech samples collected from both groups to analyze these metrics. Results Findings indicate a disparity in speaking rate and articulation rate between the AE and MAE groups, with the AE group exhibiting significantly faster speech. However, no significant differences were observed in SES and vocabulary diversity between the two groups. Conclusion The results are discussed in the context of the interplay between speaking rate, speech fluency, and vocabulary diversity. These findings shed light on the maintenance of speech efficiency among bilingual speakers, suggesting that despite differences in speaking rate and articulation rate, SES and vocabulary diversity remain comparable between native AE speakers and MAE speakers. .

Assessing GPT-4's Performance in Delivering Medical Advice: Comparative Analysis With Human Experts.

Accurate medical advice is paramount in ensuring optimal patient care, and misinformation can lead to misguided decisions with potentially detrimental health outcomes. The emergence of large language models (LLMs) such as OpenAI's GPT-4 has spurred interest in their potential health care applications, particularly in automated medical consultation. Yet, rigorous investigations comparing their performance to human experts remain sparse. This study aims to compare the medical accuracy of GPT-4 with human experts in providing medical advice using real-world user-generated queries, with a specific focus on cardiology. It also sought to analyze the performance of GPT-4 and human experts in specific question categories, including drug or medication information and preliminary diagnoses. We collected 251 pairs of cardiology-specific questions from general users and answers from human experts via an internet portal. GPT-4 was tasked with generating responses to the same questions. Three independent cardiologists (SL, JHK, and JJC) evaluated the answers provided by both human experts and GPT-4. Using a computer interface, each evaluator compared the pairs and determined which answer was superior, and they quantitatively measured the clarity and complexity of the questions as well as the accuracy and appropriateness of the responses, applying a 3-tiered grading scale (low, medium, and high). Furthermore, a linguistic analysis was conducted to compare the length and vocabulary diversity of the responses using word count and type-token ratio. GPT-4 and human experts displayed comparable efficacy in medical accuracy ("GPT-4 is better" at 132/251, 52.6% vs "Human expert is better" at 119/251, 47.4%). In accuracy level categorization, humans had more high-accuracy responses than GPT-4 (50/237, 21.1% vs 30/238, 12.6%) but also a greater proportion of low-accuracy responses (11/237, 4.6% vs 1/238, 0.4%; P=.001). GPT-4 responses were generally longer and used a less diverse vocabulary than those of human experts, potentially enhancing their comprehensibility for general users (sentence count: mean 10.9, SD 4.2 vs mean 5.9, SD 3.7; P<.001; type-token ratio: mean 0.69, SD 0.07 vs mean 0.79, SD 0.09; P<.001). Nevertheless, human experts outperformed GPT-4 in specific question categories, notably those related to drug or medication information and preliminary diagnoses. These findings highlight the limitations of GPT-4 in providing advice based on clinical experience. GPT-4 has shown promising potential in automated medical consultation, with comparable medical accuracy to human experts. However, challenges remain particularly in the realm of nuanced clinical judgment. Future improvements in LLMs may require the integration of specific clinical reasoning pathways and regulatory oversight for safe use. Further research is needed to understand the full potential of LLMs across various medical specialties and conditions.

Should We Stop Using Lexical Diversity Measures in Children's Language Sample Analysis?

Prior work has identified weaknesses in commonly used indices of lexical diversity in spoken language samples, such as type-token ratio (TTR) due to sample size and elicitation variation, we explored whether TTR and other diversity measures, such as number of different words/100 (NDW), vocabulary diversity (VocD), and the moving average TTR would be more sensitive to child age and clinical status (typically developing [TD] or developmental language disorder [DLD]) if samples were obtained from standardized prompts. We utilized archival data from the norming samples of the Test of Narrative Language and the Edmonton Narrative Norms Instrument. We examined lexical diversity and other linguistic properties of the samples, from a total of 1,048 children, ages 4-11 years; 798 of these were considered TD, whereas 250 were categorized as having a language learning disorder. TTR was the least sensitive to child age or diagnostic group, with good potential to misidentify children with DLD as TD and TD children as having DLD. Growth slopes of NDW were shallow and not very sensitive to diagnostic grouping. The strongest performing measure was VocD. Mean length of utterance, TNW, and verbs/utterance did show both good growth trajectories and ability to distinguish between clinical and typical samples. This study, the largest and best controlled to date, re-affirms that TTR should not be used in clinical decision making with children. A second popular measure, NDW, is not measurably stronger in terms of its psychometric properties. Because the most sensitive measure of lexical diversity, VocD, is unlikely to gain popularity because of reliance on computer-assisted analysis, we suggest alternatives for the appraisal of children's expressive vocabulary skill.

Speech-to-text intervention to support text production among students with writing difficulties: a single-case study in nordic countries

Studies report that speech-to-text applications (STT) may support students with writing difficulties in text production. However, existing research is sparse, shows mixed results, and lacks information on STT interventions and their applicability in schools. Therefore, this study aimed to investigate whether a systematic and intensive assistive technology intervention focusing on STT can improve text production. A modified multiple-baseline across-subject design was used involving eight middle school students, four Norwegian and four Swedish. Their STT-produced narrative texts were collected during and after the intervention and the productivity, accuracy, and text quality were analysed. Keyboarding was the baseline control condition. The results demonstrated that seven of the eight students increased text productivity and that the proportion of word-level accuracy was maintained or improved. The use of punctuation progressed in participants with poor baseline skills. Most students’ STT-produced texts had at least a similar ratio of meaningfulness and text quality as keyboarding. However, the magnitude of the changes and development patterns varied, with three students showing the most notable impacts. In conclusion, this study’s intervention seemed beneficial in initially instructing STT, and the progress monitoring guided individually adapted future interventions such as balancing productivity and formal language aspects. Removing the spelling barrier with STT provided an opportunity for students to improve their higher-order skills, such as vocabulary diversity and overall text quality. Furthermore, visible progress, such as the ability to produce longer texts, might motivate continued STT usage. However, such development may not always be immediate.

The Use of Natural Language Processing Elements for Computer-Aided Diagnostics and Monitoring of Body Image Perception in Enterally Fed Patients with Head and Neck or Upper Gastrointestinal Tract Cancers.

Psycho-oncology care has emerged as a significant concern in contemporary oncology practice, given its profound impact on patient psychological well-being. Patients undergoing treatment for head-neck or upper gastrointestinal tract cancers often experience complex emotional and psychological challenges, necessitating specialized support and intervention. Traditional approaches to psycho-oncological care may be limited in their ability to comprehensively assess and address patients' needs. Therefore, exploring innovative methodologies, such as leveraging natural language processing (NLP) elements, is crucial to enhancing the effectiveness of psycho-oncological interventions. In this study, we developed a method utilizing natural language processing (NLP) elements to augment psycho-oncological care for patients with head-neck or upper gastrointestinal tract cancers. The method aimed to facilitate vocabulary, sentiment, and intensity analysis of five basic emotions (happiness, sadness, anger, disgust, and fear), as well as to explore potential areas of difficulty such as body image, pain, and self-esteem. We conducted research involving 50 patients across three treatment stages. Our method facilitated the identification of characteristic features at each treatment stage, aiding in the tailoring of appropriate therapies to individual patient needs. The results offer insights valuable to psychologists and psychiatrists for expedited diagnosis and intervention, potentially influencing therapy outcomes. Additionally, the data may inform treatment decisions by addressing patient-specific concerns. Furthermore, our method holds promise for optimizing the allocation of psychological care resources, particularly at the initial stages of patient contact. The main problem in the research was the fairly wide age range of participants, which explains the potential diversity of vocabulary. In conclusion, our study demonstrates the potential utility of integrating natural language processing (NLP) elements into psycho-oncological care for patients with head-neck or upper gastrointestinal tract cancers. The developed method offers a novel approach to comprehensively assessing patients' emotional states and areas of difficulty, thereby facilitating tailored interventions and treatment planning. These findings underscore the importance of continued research and innovation in psycho-oncology to enhance patient care and outcomes.

Cigarette tasting Chinese text classification for low-resource scenarios

At present, the Chinese text field is facing challenges from low resource issues such as data scarcity and annotation difficulties. Moreover, in the domain of cigarette tasting, cigarette tasting texts tend to be colloquial, making it difficult to obtain valuable and high-quality tasting texts. Therefore, in this paper, we construct a cigarette tasting dataset (CT2023) and propose a novel Chinese text classification method based on ERNIE and Comparative Learning for Low-Resource scenarios (ECLLR). Firstly, to address the issues of limited vocabulary diversity and sparse features in cigarette tasting text, we utilize Term Frequency-Inverse Document Frequency (TF-IDF) to extract key terms, supplementing the discriminative features of the original text. Secondly, ERNIE is employed to obtain sentence-level vector embedding of the text. Finally, contrastive learning model is used to further refine the text after fusing the keyword features, thereby enhancing the performance of the proposed text classification model. Experiments on the CT2023 dataset demonstrate an accuracy rate of 96.33% for the proposed method, surpassing the baseline model by at least 11 percentage points, and showing good text classification performance. It is thus clear that the proposed approach can effectively provide recommendations and decision support for cigarette production processes in tobacco companies.

Beyond content: discriminatory power of function words in text type classification

Abstract Our work aims to evaluate the strength of the association between function words and several text types: novels, poems, academic articles, reviews, and blog posts, and the accuracy of their classification to these categories, through machine-learning and statistical methods. The principal conclusion is that the types of texts are distinguishable based only on the function words, either by vocabulary or vocabulary diversity. Such findings may impact the techniques of authorship attribution based on function words and text clustering techniques since some function words add information about the text types/genres, in addition to content words.

Expanding the Spatial Reach and Human Impacts of Critical Zone Science

AbstractTwo major barriers hinder the holistic understanding of subsurface critical zone (CZ) evolution and its impacts: (a) an inability to measure, define, and share information and (b) a societal structure that inhibits inclusivity and creativity. In contrast to the aboveground portion of the CZ, which is visible and measurable, the bottom boundary is difficult to access and quantify. In the context of these barriers, we aim to expand the spatial reach of the CZ by highlighting existing and effective tools for research as well as the “human reach” of CZ science by expanding who performs such science and who it benefits. We do so by exploring the diversity of vocabularies and techniques used in relevant disciplines, defining terminology, and prioritizing research questions that can be addressed. Specifically, we explore geochemical, geomorphological, geophysical, and ecological measurements and modeling tools to estimate CZ base and thickness. We also outline the importance of and approaches to developing a diverse CZ workforce that looks like and harnesses the creativity of the society it serves, addressing historical legacies of exclusion. Looking forward, we suggest that to grow CZ science, we must broaden the physical spaces studied and their relationships with inhabitants, measure the “deep” CZ and make data accessible, and address the bottlenecks of scaling and data‐model integration. What is needed—and what we have tried to outline—are common and fundamental structures that can be applied anywhere and used by the diversity of researchers involved in investigating and recording CZ processes from a myriad of perspectives.

Variasi dan Komponen Makna Verba Pewarta pada Korpus Berita Daring

"Reporting verbs indicate a conversation (Romli, 2018:35). In news texts, reporting verbs are used intensively to provide information. This research focuses on the identification of variations, frequencies, and the analysis of meaning components as proposed by Nida (1975) regarding the usage of reporting verbs in online news corpus. This research uses a qualitative descriptive method with a lexical semantic approach. Data were obtained from news texts published by Kompas.com and Detik.com during August 8-10, 2023, with the theme 'Ferdy Sambo's Death Sentence Turned into Life Imprisonment.' Based on the research findings, 17 variations of reporting verbs were found in the news corpus, with the verb 'mengatakan' (to say) being the most dominant with a frequency of 83 occurrences. Meanwhile, based on the analysis of meaning components for the verbs 'mengatakan'(to say), 'menyatakan' (to state), 'menjelaskan' (to explain), and 'menyebut' (to mention), differences in usage were found through the differentiation of the meaning components of each reporting verb. This research is expected to contribute by highlighting the differences in meaning among synonymous verbs to demonstrate the diversity of the Indonesian language vocabulary.

Examining gender effects in autistic written language skills: A small sample exploratory study.

Gender differences in the written language of autistic individuals are an overlooked but important area of research. We contend that the gender differences in spoken language of autistic individuals may extend to written language, mirroring the gender differences of writing in the general population and reflecting the shared dimensionality of oral and written language. Our research question was: Do autistic adolescent females demonstrate written language characteristics, across persuasive, expository, and narrative genres, that are distinct from those of autistic adolescent males and non-autistic (NA) adolescent females? We performed a secondary, exploratory analysis on writing samples collected from 18 participants (11 autistic males, three autistic females, and four NA females) from a larger investigation of autistic adolescents' writing skills. Each participant completed three writing samples-one persuasive, one expository, and one narrative (for a total of 54 writing samples). We compared sample length (total number of words), writing productivity (words written per minute), syntactic length (mean length of T-unit in words), vocabulary diversity (type-token ratio), and macrostructure of autistic females' samples to autistic males' and NA females' samples. Based on non-parametric analyses using variable medians, autistic males, but not autistic females, wrote significantly shorter expository samples than NA females. Autistic males' writing productivity was significantly lower in the persuasive and expository genres than both autistic females and NA females. Several other comparisons of sample length, productivity, vocabulary diversity, and persuasive and narrative macrostructure yielded large effect sizes but were not statistically significant. Though our small sample sizes prevent us from drawing generalizable conclusions, we observed that some gender-specific findings of the current study differ from previous findings based on a single autistic group (females and males combined). Combining data of autistic females with autistic males may cloud the distinct written language characteristics of each group. Our findings, especially when situated in the context of relevant literature, suggest that larger-scale investigation of gender differences in written language is essential in order to more fully describe the unique characteristics of autistic females. Clinicians should be prepared to support autistic writers' needs for producing written language to meet their developmental, academic, social, and employment-related goals.

Evaluating the Impact of Text Data Augmentation on Text Classification Tasks using DistilBERT

Data augmentation entails artificially expanding the dataset's size by applying various transformations to the existing raw data. Enhancing the quality and quantity of the datasets with varying sizes by employing varieddata augmentation techniques has immense importance in the field on Natural Language Processing. Several notable applications for instance text classification, sentiment analysis, text summarization, etc. have proven to be benefitted immensely with the employment of text augmentation techniques. Hence, the paper focuses on efficient text classification using varied datasets of different sizes; small- 500 instances, medium-5564 instances and large-43934 instances.The work considers the standard DistilBERT model, a popular transformer-based language model and presents the impact on the performance of the modelafter employing different text augmentation techniques. The study specifically focuses on three augmentation methods: (a) Synonym augmentation:that involves replacing words with their synonyms to enhance vocabulary diversity and generalization, (b) Contextual word embeddings that enriches semantic understanding by leveraging pre-trained language models, and (c) Black translation that entails translating the text into a another different language and then translating it back, introducing variations in the data and capturing different linguistic patterns.Additionally,the work also discusses the combined effect of employing all three augmentation techniques simultaneously. Moreover, the study also aims compares the relation between the dataset sizes and the performance of the augmentation techniques. The study considers three standard datasets for the analysis and presents a comprehensive analysis using accuracy and F1 score as evaluation metrics. The results highlight the efficacy of each technique across small, medium, and large datasets, enabling a nuanced understanding of their benefits in different data scenarios. The findings indicate the varying degrees of improvement achieved through each augmentation technique.The enhancement achieved by applying text augmentation varied from around 2% on large datasets to 20% on smaller datasets.

Lexical diversity as a predictor of complexity in textbooks on the Russian language

The parametric model of the text as a research problem is of paramount importance in modern linguistics and education, since it opens up new approaches to understanding the processes of comprehending texts of various types. In the current study, 17 Russian language textbooks for elementary school were employed to identify correlations between lexical diversity indices and other complexity predictors. The total volume of the corpus compiled for the study is 439,938 words. The two-stage research algorithm included the evaluation of the reference values of text features at the basic level (word length, sentence length, the number of unique, non-repeating words and the number of word forms), evaluation and subsequent contrasting of complexity predictors, i.e. lexical diversity and readability indices. All calculations were performed with the automatic text analyzer RuLingva. The study revealed a positive dynamic of readability and no evidence of lexical diversity increase across grades. An average level of vocabulary diversity and overlaps of every 4th word in the text are fixed. No indication of correlation between text readability and lexical diversity is found. The obtained results can be useful to researchers, textbook authors, and teachers selecting textbooks. The prospects are seen in implementing functional and epidigmatic stratification of the vocabulary of the Russian textbooks under study.

The Use of Natural Language Processing for Computer-Aided Diagnostics and Monitoring of Body Image Perception in Patients with Cancers.

Head and neck cancers (H&NCs) constitute a significant part of all cancer cases. H&NC patients experience unintentional weight loss, poor nutritional status, or speech disorders. Medical interventions affect appearance and interfere with patients' self-perception of their bodies. Psychological consultations are not affordable due to limited time. We used NLP to analyze the basic emotion intensity, sentiment about one's body, characteristic vocabulary, and potential areas of difficulty in free notes. The emotion intensity research uses the extended NAWL dictionary developed using word embedding. The sentiment analysis used a hybrid approach: a sentiment dictionary and a deep recursive network. The part-of-speech tagging and domain rules defined by a psycho-oncologist determine the distinct language traits. Potential areas of difficulty were analyzed using the dictionaries method with word polarity to define a given area and the presentation of a note using bag-of-words. Here, we applied the LSA method using SVD to reduce dimensionality. A total of 50 cancer patients requiring enteral nutrition participated in the study. The results confirmed the complexity of emotions in patients with H&NC in relation to their body image. A negative attitude towards body image was detected in most of the patients. The method presented in the study appeared to be effective in assessing body image perception disturbances, but it cannot be used as the sole indicator of body image perception issues. The main problem in the research was the fairly wide age range of participants, which explains the potential diversity of vocabulary. The combination of the attributes of a patient's condition, possible to determine using the method for a specific patient, can indicate the direction of support for the patient, relatives, direct medical personnel, and psycho-oncologists.

Assessing the Use of German Claims Data Vocabularies for Research in the Observational Medical Outcomes Partnership Common Data Model: Development and Evaluation Study.

National classifications and terminologies already routinely used for documentation within patient care settings enable the unambiguous representation of clinical information. However, the diversity of different vocabularies across health care institutions and countries is a barrier to achieving semantic interoperability and exchanging data across sites. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) enables the standardization of structure and medical terminology. It allows the mapping of national vocabularies into so-called standard concepts, representing normative expressions for international analyses and research. Within our project "Hybrid Quality Indicators Using Machine Learning Methods" (Hybrid-QI), we aim to harmonize source codes used in German claims data vocabularies that are currently unavailable in the OMOP CDM. This study aims to increase the coverage of German vocabularies in the OMOP CDM. We aim to completely transform the source codes used in German claims data into the OMOP CDM without data loss and make German claims data usable for OMOP CDM-based research. To prepare the missing German vocabularies for the OMOP CDM, we defined a vocabulary preparation approach consisting of the identification of all codes of the corresponding vocabularies, their assembly into machine-readable tables, and the translation of German designations into English. Furthermore, we used 2 proposed approaches for OMOP-compliant vocabulary preparation: the mapping to standard concepts using the Observational Health Data Sciences and Informatics (OHDSI) tool Usagi and the preparation of new 2-billion concepts (ie, concept_id >2 billion). Finally, we evaluated the prepared vocabularies regarding completeness and correctness using synthetic German claims data and calculated the coverage of German claims data vocabularies in the OMOP CDM. Our vocabulary preparation approach was able to map 3 missing German vocabularies to standard concepts and prepare 8 vocabularies as new 2-billion concepts. The completeness evaluation showed that the prepared vocabularies cover 44.3% (3288/7417) of the source codes contained in German claims data. The correctness evaluation revealed that the specified validity periods in the OMOP CDM are compliant for the majority (705,531/706,032, 99.9%) of source codes and associated dates in German claims data. The calculation of the vocabulary coverage showed a noticeable decrease of missing vocabularies from 55% (11/20) to 10% (2/20) due to our preparation approach. By preparing 10 vocabularies, we showed that our approach is applicable to any type of vocabulary used in a source data set. The prepared vocabularies are currently limited to German vocabularies, which can only be used in national OMOP CDM research projects, because the mapping of new 2-billion concepts to standard concepts is missing. To participate in international OHDSI network studies with German claims data, future work is required to map the prepared 2-billion concepts to standard concepts.

Parent–toddler play talk: Toddler speech is differentially associated with paternal and maternal speech in interaction

Children’s speech is influenced by the speech they hear, in particular by the parental speech addressed directly to them. The aim of this study was to analyse toddlers’ speech with their parents and to investigate the influence of specific characteristics of child-directed speech on child speech in real time during mother–child and father–child dyadic interactions. Eighty-four children ( M = 24.11 months, SD = 1.37, 42 girls) and their parents were video-recorded in dyadic freeplay. Parent and child speech was compared on measures of quantity (number of words), quality (vocabulary diversity, proportion, and composition of nouns/verbs) and productivity or complexity. Overall findings reveal very similar speech produced by mothers and fathers with their toddlers and by toddlers with each parent, with the exception of vocabulary diversity patterns. Furthermore, the child-directed speech of mothers and fathers was differently associated with child speech in interaction. Findings suggest that children may be exposed to different lexical sets by mother and father and there may be added value for the child of engaging in separate dyadic interactions with mother and father. Implications for research include sampling child speech across contexts and conversational partners.

북한 공장대학교 영어교과서 읽기지문에 대한 코퍼스 기반 분석

This paper aims to analyze reading passages found in an English textbook used at Factory College in North Korea and examine the characteristics of English education for adults, particularly “learning while working system” implemented in North Korea. To investigate the linguistic properties; lexical frequency(high frequency vocabulary, keywords, and 4gram) and readability(measured by FKGL and FRE), this corpus-based analysis was conducted with Wordsmith, AntConc and Readable. The findings demonstrate frequent appearance of science and technology terms, and their frequent description. This confirms that North Korea attempts to connect education with industrial sites by combining English and technical education. In addition, as a result of analyzing vocabulary diversity and readability, it was found that the level of difficulty increased rapidly compared to high school English textbooks. The vocabulary appeared less varied than English reading passages of Korean SAT and readability showed a similar level.

언어반응촉진 전략을 사용한 보완대체의사소통 중재가 자폐성장애 중학생의 구문능력과 어휘다양도에 미치는 효과

The purpose of this study is to analyze the effects of augmentative and alternative communication (AAC) intervention using strategies prompting language response on syntax ability (grammatical morpheme, mean length of utterance) and vocabulary diversity (the number of total words, the number of different words) of middle school students with autism. Participants in this study were two middle school students with autism who talked through just some single words and had little spontaneous speech. In this study, the independent variables were strategies prompting language response (expansion, recast with contrast, modeling with mand, and time delay), no-tech AAC aid, story-grammar map, and familiar and experienced photos. Dependent variables were grammatical morphemes (subject case marker), (object case marker), (locative case marker), Mean length of utterance (MLU-m, MLU-w, MLU-c) and vocabulary diversity. Multiple probe baseline design across behaviors was conducted and the interventions were given three sessions a week. As a result of this study, AAC intervention using strategies prompting language response was effective on increasing grammatical morphemes and MLU of middle school students with autism. In addition to the AAC intervention using strategies prompting language response was effective on vocabulary diversity of middle school students with autism.

Vocabulary Diversity in Personal Narratives Produced in Response to the Global TALES Protocol in Dutch-Speaking Students with and without Dyslexia

Introduction: This study examines whether there are differences in expressive vocabulary between participants with and without dyslexia in personal narratives in response to the Global TALES protocol. Methods: 22 monolingual Dutch-speaking participants aged 11–16 with dyslexia and 22 age and gender-matched peers without dyslexia were assessed on measures of decoding, reading comprehension, and spelling of words, pseudowords, verbs, and sentences. The participants also produced personal narratives in response to the six prompts contained in the Global TALES protocol. We analyzed the personal narratives for expressive vocabulary and counted the total number of different words (TNDW). Results: The study revealed a significant relationship between TNDW and reading comprehension (r = 0.45, p = 0.002, BF₁₀ = 17.70), spelling words (r = 0.42, p = 0.005, BF₁₀ = 8.93), and spelling and writing conventions in sentences (r = 0.37, p = 0.016, BF₁₀ = 3.11). The Global TALES protocol was successful in eliciting personal narratives in the Dutch-speaking participants with and without dyslexia. Participants with dyslexia used fewer different words (M = 192.27, SD = 64.37; 95% CI: [151.84–232.71]) compared to peers without dyslexia (M = 265.50, SD = 116.28; 95% CI: [225.06–305.93]; F(1, 42) = 6.68; p = 0.013; η² = 0.14). When we compared the probability of models, Bayesian factors revealed moderate evidence for group differences in TNDW (BF = 3.94). Conclusion: Our findings indicate that older school-age participants with dyslexia may lag behind their peers in expressive vocabulary in a personal narrative discourse task that is relevant to everyday functioning. The results of this study highlight the relationship between expressive vocabulary and reading comprehension and the importance of the assessment of spoken language skills in children with dyslexia. Reading problems might lead to less advanced spoken language, which in turn may negatively affect the expressive vocabulary growth in individuals with dyslexia.