Item Wording Research Articles

Arabic word tokenization system using the maximum matching model

Word tokenization is the first stage for higher-order Natural Language Processing (NLP) tasks like Part-of-Speech (PoS) tagging, parsing, and named entity recognition. The amount of text on the World Wide Web is growing daily in the present era of technology, necessitating the use of advanced instruments. Since more and more people speak Arabic around the world, Arabic language processing systems must be improved. Due to the writing style of Arabic with a lack of support for capitalization features and the use of compound words, it is difficult to perform word tokenization. This research paper proposes a novel Arabic word tokenization system based on the knowledge. To develop this system, a maximum matching model with its two variations, namely forward and reverse maximum matching is used. The proposed system is implemented in Python. The results produced during system evaluation report high performance.

Development of Digital Health Gender and Sexual Health Literacy scale

Abstract Introduction Health literacy, as the ability to obtain, process and understand health information, including gender and sexuality awareness, appears to be underdeveloped in terms of measurement tools and thematic materials. The literature indicates the existence of serious deficits in gender and sexual health literacy at all levels, with implications for relationships with citizens and patients. Methods Delphi study agreement was conducted and a pool of potential scale items was generated, reflecting a range of skills and abilities within each domain. Items were carefully worded to ensure clarity, cultural sensitivity and relevance to different gender identities and sexual orientations. Expert review and cognitive pre-testing were employed to refine item wording, assess content validity, and ensure that the 10-item scale was comprehensible and relevant to the target population. Results The theoretical development of the 10-item Digital Health Gender and Sexual Health Literacy Scale was successfully completed. The scale items were designed to provide a comprehensive measure of an individual’s ability to navigate the digital landscape for gender and sexual health information. Conclusions The theoretical validation of the Digital Health Gender and Sexual Health Literacy Scale (DiGeSHeL) provides the community with a promising tool to assess individuals’ confidence and ability to use digital technologies for sexual health information and resources. However, further research is needed to validate the scale and test its reliability with a diverse group of participants. Once validated, this scale could be a valuable tool for sexual health promotion programmes to identify gaps and evaluate the impact of digital literacy interventions. Key messages • Gender and sexual literacy are crucial for health. • Health literacy as six vital sign.

A default mode network subsystem supports both item and associative word encoding: Insights from a meta-analysis

Abstract Recent meta-analytic evidence has underscored the significant role of the default mode network (DMN) in facilitating item word encoding. This study builds on this finding through a comprehensive meta-analysis of fMRI-based subsequent memory studies that use words as stimuli. The results highlight several key functions within the DMN. Firstly, the dorsomedial prefrontal cortex (dmPFC) subsystem of the DMN plays a pivotal role in enhancing successful word encoding, suggesting its vital involvement in the semantic processing of incoming verbal information. Secondly, the utility of the dmPFC subsystem extends beyond item word encoding to associative word encoding tasks, demonstrating its broad applicability in verbal information encoding. Thirdly, regions within the left inferior frontal cortex, a core component of the dmPFC subsystem, show increased activity during associative compared to item word encoding, emphasizing their role in integrating verbal information with contextual details. Contrary to previous research that linked the DMN with encoding interference—often attributed to the core subsystem’s tendency for mind-wandering—this study highlights the facilitative role of the dmPFC subsystem in memory encoding. The contrasting roles of the DMN subsystems, both interfering and facilitating, challenge traditional views and advocate for a more nuanced understanding of the network’s role in memory encoding.

Lexical frequency and target language input in the primary languages classroom

ABSTRACT This paper presents an exploratory study of L2 input in the primary languages classroom. Teacher talk in 33 videorecorded lessons taught to Year 3 French beginners by a specialist teacher of French was analysed, in order to document the extent and nature of L2 vocabulary input. Further analyses compared the lexis of teacher talk in this primary setting with high-frequency wordlists derived from two reference corpora designed to support the teaching of L2 French. Results showed that children were exposed to c 45,000 words (tokens) of French, reflecting a teacher vocabulary of c 650 lemmas. Intentionally taught lexis such as foods or animal names typically received massed practice, not always supplemented by distributed practice. The teachers’ partial but consistent use of French for classroom management contributed to a well-balanced distribution of word classes. There was considerable overlap with both reference wordlists, and divergence could largely be explained by the teacher’s choice of topics, and incidental use of French to personalise and engage childrens’ individual interests, as well as for classroom management purposes. In discussion, the need for careful planning and management of vocabulary instruction is highlighted, as well as the significant potential of L2 classroom management to enrich lexical input.

Development of the Compassionate Technology Scale for Professionals (CTS-P): value driven evaluation of digital mental health interventions

BackgroundCompassion is an essential and beneficial value in mental healthcare. However, how digital mental health interventions influence compassion in treatment has not been systematically investigated, due to the lack of appropriate measurement instruments. To address this gap, we developed the Compassionate Technology Scale for Professionals (CTS-P), aimed at mental health professionals.MethodsWe used Q-methodology, a method that combines quantitative and qualitative analysis to explore shared viewpoints on a particular topic, to select and refine items. Participants were 15 professionals from different areas of mental healthcare. In individual sessions, while thinking aloud, they sorted 35 statements on relevance for evaluating technology use on compassion. The statements were based on a scientific conceptualization of compassion with five elements. The sorting task was followed by a short interview to explore participants’ associations with compassion and technology.ResultsWith by-person factor analysis, we found three different viewpoints among participants, all with eigenvalues > 1 and with a total explained variance of 63.02%. We selected prioritized items of each viewpoint and for each theoretical element of compassion, resulting in a full scale (15 items) and a short scale (3 items). Based on qualitative input from participants, the scale was adapted to clarify its focus and the wording of items. While thinking aloud, participants shared benefits and critical notes regarding technology and compassion.DiscussionTogether with key stakeholders, we developed the CTS-P. Most participants prioritized the potential of technology to obtain more information and be closer to their client in facilitating compassion. The main critical note participants had was that technology is not necessary to support compassion at the therapist’s experiential level. This emphasizes the need to further explore how mental health professionals and technology can complement each other in a system of compassionate care. Future research should explore the factor structure, validity, and reliability of the scale through psychometric validation.ConclusionsThe CTS-P can make the extent to which technology for mental healthcare influences elements of compassion measurable and comparable. The scale can prove useful for prioritizing the value of compassion in both the development and use of digital mental health interventions.

Assessing Jealousy: Factor Analyses, Measurement Invariance, Nomological Validity, and Longitudinal APIM Analyses of the Multidimensional Jealousy Scale.

The Multidimensional Jealousy Scale is the standard instrument to assess cognitive, emotional, and behavioral jealousy. We examined competing factor models and external correlations with broad and narrow traits. Across two studies, we analyzed four samples (Ntotal = 2,117). Confirmatory factor analysis supported the measurement model of three correlated factors in comparison to unidimensional, second-order, and bifactor models. Thus, speaking against the use of a total score. Furthermore, we found measurement invariance between romantic partners. We extended the Multidimensional Jealousy Scale (MJS)' nomological net to personality pathology and replicated prior findings of associations with broad and narrow traits. Study 2 examined longitudinal data (5- to 9-month lag) from couples. Actor-Partner Interdependence Model analyses showed that the MJS predicts facets of relationship satisfaction in actors and partners. We discuss potential avenues for revising the MJS (e.g., heteronormative item wordings).

Aspect Based Feature Extraction in Sentiment Analysis using Bi-GRU-LSTM Model

In Natural Language Processing (NLP), Sentiment Analysis (SA) is a fundamental process which predicts the sentiment expressed in sentences. In contrast to conventional sentiment analysis, Aspect-Based Sentiment Analysis (ABSA) employs a more nuanced approach to assess the sentiment of individual aspects or components within a document or sentence. Its objective is to identify the sentiment polarity, such as positive, neutral, or negative, associated with particular elements disclosed within a sentence. This research introduces a novel sentiment analysis technique that proves to be more efficient in sentiment analysis compared to current methods. The suggested sentiment analysis method undergoes three key phases: 1. Pre-processing 2. Extraction of aspect sentiment and 3. Sentiment analysis classification. The input text data undergoes pre-processing through the implementation of four typical text normalization techniques, which include stemming, stop word elimination, lemmatization, and tokenization. By employing these methods, the provided text data is prepared and fed into the aspect sentiment extraction phase. During the aspect sentiment extraction phase, features are obtained through a series of steps, including enhanced ATE (Aspect Term Extraction), assessment of word length, and determination of cosine similarity. By following these steps, the relevant features are extracted on the basis of aspects and sentiments involved in the text data. Further, a hybrid classification model is proposed to classify sentiments. In this work, two of the Deep Learning (DL) classifiers, Bi-directional Gated Recurrent Unit (Bi-GRU) and Long Short-Term memory (LSTM) are used in proposing a hybrid classification model which classifies the sentiments effectively and provides accurate final predicted results. Moreover, the performance of proposed sentiment analysis technique is analyzed experimentally to show its efficacy over other models.

Fake News Detection Using Optimized Convolutional Neural Network and Bidirectional Long Short-Term Memory

The spread of fake news in the digital age threatens the integrity of online information, influences public opinion, and creates confusion. This study developed and tested a fake news detection model using an enhanced CNN-BiLSTM architecture with GloVe word embedding techniques. The WELFake dataset comprising 72,000 samples was used, with training and testing data ratios of 90:10, 80:20, and 70:30. Preprocessing involved GloVe 100-dimensional word embedding, tokenization, and stopword removal. The CNN-BiLSTM model was optimized with hyperparameter tuning, achieving an accuracy of 96%. A larger training data ratio demonstrated better performance. Results indicate the effectiveness of this model in distinguishing fake news from real news. This study shows that the CNN-BiLSTM architecture with GloVe embedding can achieve high accuracy in fake news detection, with recommendations for further research to explore preprocessing techniques and alternative model architectures for further improvement.

Routine Language Intervention: Supporting Spanish-Speaking Mothers’ Talk to Toddlers During Everyday Routines

SYNOPSIS Objective. Toddlers learn language as they participate in everyday routines in the home environment. The Routine Language Intervention encourages caregivers to infuse language in everyday routines to support toddlers’ language development. Design. Researchers collaborated with a Spanish-speaking, immigrant community in New York City to develop an intervention that is grounded in the routines that mothers commonly engage in with their toddlers. Altogether, 51 mothers participated in 6 months of group workshops and home visits. To test the efficacy of the intervention, mothers were videorecorded interacting with their toddlers at home before and after participating in the intervention. A comparison group of 12 families drawn from the same community was also videorecorded in their homes. Results. Mothers who participated in the Routine Language Intervention increased their speech to toddlers, nearly doubling word tokens and word types, and used more types and tokens than mothers in the matched comparison group. Increases in maternal speech were seen for words referring to targeted daily routines (e.g., mealtime, grooming, play and literacy) and extended to other words (e.g., unrelated household objects). Conclusions. Routine Language offers a culturally sensitive, easy-to-implement intervention that builds on families’ everyday routines and yields benefits for toddlers’ early language exposure.

The Inventory of Callous-Unemotional Traits (ICU) self-report version: Factor structure, measurement invariance, and predictive validity in justice-involved male adolescents.

The Inventory of Callous-Unemotional Traits (ICU) is a widely used measure of callous-unemotional (CU) traits that may aid in the assessment of the diagnostic specifier "with limited prosocial emotions," which has been added to diagnostic criteria for conduct disorder. Though there is substantial support for use of the ICU total score, the scale's factor structure has been highly debated. Inconsistencies in past factor analyses may be largely attributed to failure to control for method variance due to item wording (i.e., half of the items being worded in the callous direction and half worded in the prosocial direction). Thus, the present study used a multitrait-multimethod confirmatory factor analytic approach that models both trait and method variance to test the factor structure of the ICU self-report in a clinically relevant, high-risk sample of justice-involved male adolescents (N = 1,216). When comparing the fit of empirical and theoretical models, goodness of fit indices (χ² = 1105.877, df = 190, root-mean-square error of approximation = .063, comparative fit index = .916, Tucker-Lewis index = .878, standardized root-mean-square residual = .051) provided support for a hierarchical four-factor model (i.e., one overarching callous-unemotional factor, four latent trait factors) when accounting for method variance (i.e., covarying positively worded items). This factor structure is consistent with the way the ICU was constructed and with criteria for the limited prosocial emotions specifier. In addition, measurement invariance of this factor structure across age, race, and ethnicity was supported, and the predictive validity of the ICU was supported across these demographic groups in predicting self-reported antisocial behavior and rearrests over a 5-year period following an adolescent's first arrest. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

German translation and cross-cultural adaptation of the Vestibular Schwannoma Quality of Life Index (VSQOL)

BackgroundVestibular schwannomas (VSs) are benign tumors of the vestibulocochlear nerve that often cause significant neurological and functional impairment. Patient-reported outcomes, including quality of life (QoL), are essential for understanding the overall impact of VS and its treatment. This study aimed to translate and culturally adapt the Vestibular Schwannoma Quality of Life (VSQOL) Index into German to expand its relevance to German-speaking populations.MethodsWe used a qualitative approach including translation and cognitive interviews with 10 patients who underwent VS surgery. The translation process followed the TRAPD protocol to ensure linguistic and conceptual accuracy. Cognitive interviews assessed the comprehensibility and relevance of the translated questionnaire.ResultsThe translation showed remarkable consistency between translators, with minor discrepancies resolved by consensus. Cognitive interviews provided valuable insights that led to refinements in item wording. Participants emphasized the importance of an additional item on physician referrals, reflecting differences in health care systems between the United States and Germany.ConclusionsThe German VSQOL provides a comprehensive tool for assessing QoL in patients with VS that integrates patient-centered dimensions. A Validation study is underway to establish its reliability and validity.

Factor Structure and Reliability of the Lithuanian Version of the Public Speaking Anxiety Scale

Social Anxiety Disorder (SAD) is especially prevalent among young individuals aged 18–25 and significantly affects daily social activities and interpersonal relationships. Public Speaking Anxiety (PSA), a subtype of SAD, is a widespread concern that affects one in five individuals. The study focuses on the Public Speaking Anxiety Scale (PSAS), with the aim of assessing the factor structure and reliability of the Lithuanian version (PSAS-LT). The PSAS-LT, administered to 227 participants aged 18–25, comprises 17 Likert-scaled items, evaluating cognitive, behavioral, and physiological aspects of PSA. Three models were tested: a single-factor model, a three-factor model and a single factor model with positive and negative item wording factors model. Results indicate less than desirable fit for the single, and three-factor models, suggesting the need for alternative structures. The model that included a single factor as well as positive and negative item wording factors demonstrated a reasonably good fit. The diagnostic validity confirmed that the PSAS-LT effectively differentiated between participants with and without history of anxiety disorders. The total score of the PSAS-LT had excellent internal consistency. Despite limitations, including convenience sampling and nonrepresentative sample, the study contributes valuable insights into refining the understanding of PSA assessment features, emphasizing the importance of considering response patterns. Future research should validate these findings with larger and more diverse samples of the Lithuanian population.

Long-term cognitive and affective consequences of mild traumatic brain injury: comparison with older adults

ABSTRACT Objective Memory and affective processing were compared in young adults with a remote mild traumatic brain injury (mTBI), to healthy younger and older adults. We evaluated memory performance when encoding was done under multi-tasking (divided attention) conditions, likely to exacerbate cognitive and psychological symptoms in mTBI. Methods Participants studied pairs of unrelated words under either full or divided attention conditions. Memory for single words (item memory) and for pairs of words (associative memory) was then assessed in sequential independent recognition tests, under full attention. Results Associative memory was poorer than item memory, and worse when encoding was done under divided than full attention. The decline in recognition accuracy from full to divided attention conditions on the associative memory test was significantly greater in mTBI compared to young adults and was similar in magnitude to that observed in older adults under full attention. Self-reported mental and total fatigue increased significantly as performance on the memory tests, following the divided attention condition, decreased, but only in the mTBI group. Conclusions Results show lingering memory deficits, and suggest that cognitive tasks may be experienced as psychologically more demanding in those with a mTBI, even months or years after injury.

Exploring cross-linguistic differences in parental input and their associations with child expressive language in ASD: Bulgarian versus English comparison.

Parental input plays a central role in typical language acquisition and development. In autism spectrum disorder (ASD), characterized by social communicative and language difficulties, parental input presents an important avenue for investigation as a target for intervention. A rich body of literature has identified which aspects of grammatical complexity and lexical diversity are most associated with child language ability in both typical development and autism. Yet, the majority of these studies are conducted with English-speaking children, thus potentially overlooking nuances in parental input derived from cross-linguistic variation. To examine the differences in verbal parental input to Bulgarian- and English-speaking children with ASD. To examine whether aspects of verbal parental input found to be concurrent predictors of English-speaking children's expressive language ability are also predictors of the expressive language of Bulgarian-speaking children with ASD. We compared parental input to Bulgarian-speaking (N=37; 2;7-9;10 years) and English-speaking (N=37; 1;8-4;9 years) children with ASD matched on expressive language. Parent-child interactions were collected during free play with developmentally appropriate toys. These interactions were transcribed, and key measures of parental input were extracted. English-speaking parents produced more word tokens and word types than Bulgarian-speaking parents. However, Bulgarian parents produced more verbs in relation to nouns and used more statements and exclamations but asked fewer questions than English-speaking parents. In addition, child age and parents' use of questions were significant concurrent predictors of child expressive vocabulary. This is one of the first studies to conduct a cross-linguistic comparison of parental input in ASD. The differences found emphasize the need to further study parental input to Bulgarian children and adapt naturalistic parent-mediated interventions to the local language and its specific characteristics. What is already known on the subject A rich body of literature has identified the specific aspects of grammatical complexity, lexical diversity, and question-asking that are concurrently and longitudinally associated with the language ability of children with typical development and of children with ASD. Yet, the majority of these studies are conducted with English-speaking children. What this paper adds to the existing knowledge The present study finds that there are specific differences in verbal parental input to Bulgarian- and English-speaking children with autism in terms of lexical composition and question-asking. Bulgarian parents used more verbs than nouns, and the opposite pattern was found for English-speaking parents. In addition, Bulgarian parents asked fewer questions but used more statements and exclamations. Nevertheless, parental question use was significantly correlated with children's language ability across both groups, suggesting that question-asking should be further examined as a potential target for parent-mediated language interventions for Bulgarian children with autism. What are the potential or actual clinical implications of this work? Most language and social communication interventions for autism are designed and piloted with English-speaking children. These interventions are often simply translated and used in different countries, with different populations and in different contexts. However, considering that one of the defining characteristics of autism is language difficulty, more studies should examine (1) how these language difficulties manifest in languages other than English, and (2) what characterizes verbal parental input in these other contexts. Such research investigations should inform future language and social communication interventions. The present study emphasizes the cross-linguistic differences between Bulgarian- and English-speaking parents' verbal input to their children with autism.

A - 45 Rasch Analysis of the Brief Resilience Scale in Adolescents with Sports-Related Concussion

Abstract Purpose Accurate measurement of latent abilities is essential for valid inference. The Brief Resilience Scale (BRS) assesses the “ability to bounce back”from stressful situations, and resilience may play a role in concussion recovery. Questions regarding the factor structure of the BRS have been explored in other populations, but evidence of its measurement quality in concussion is lacking. The present study investigated the psychometric properties of the BRS in concussed adolescents within a Rasch framework. Method 927 participants aged 12 to 18 (M = 14.7; 50.3% female) underwent clinical evaluation within 30 days of injury in the ConTex study. The BRS contains 6 items rated on a 5-point Likert scale (Strongly Disagree to Strongly Agree), half of which are reverse scored. The Rasch Rating Scale Model was applied and item fit statistics and dimensionality were examined to assess the degree of fit of the data to the Rasch model. Results Item difficulties ranged −0.82 to 0.77. Item fit was adequate based on mean-square infit and outfit values. The BRS demonstrated sufficient targeting except for participants at the highest and lowest range of latent ability. Unidimensionality testing indicated the presence of a secondary dimension. Conclusions The BRS showed suboptimal fit with the Rasch model based on lack of unidimensionality. However, violations likely reflect item wording effects and not a substantive dimension that merits scale modification. Application of Rasch and other item response theory methodologies can enable development of more psychometrically sound and clinically informative tools for use in the context of concussion outcomes.

Could this be next for corpus linguistics? Methods of semi-automatic data annotation with contextualized word embeddings

Abstract This paper explores how linguistic data annotation can be made (semi-)automatic by means of machine learning. More specifically, we focus on the use of “contextualized word embeddings” (i.e. vectorized representations of the meaning of word tokens based on the sentential context in which they appear) extracted by large language models (LLMs). In three example case studies, we assess how the contextualized embeddings generated by LLMs can be combined with different machine learning approaches to serve as a flexible, adaptable semi-automated data annotation tool for corpus linguists. Subsequently, to evaluate which approach is most reliable across the different case studies, we use a Bayesian framework for model comparison, which estimates the probability that the performance of a given classification approach is stronger than that of an alternative approach. Our results indicate that combining contextualized word embeddings with metric fine-tuning yield highly accurate automatic annotations.

Internal State Terms in the Narratives of Bilingual Children With Developmental Language Disorder: The Role of Microstructure and Macrostructure.

We examined the role of narrative microstructure (production of words and sentences) and narrative macrostructure (organization of events) in the use of internal state terms (ISTs) in narratives of bilingual children with developmental language disorder (DLD) in their school language (SL). Fifty-eight Russian-Hebrew bilingual preschool children aged 55-78 months divided into two groups participated: 16 bilingual children diagnosed with bilingual DLD and 42 with typical language development (TLD). Narratives were elicited in SL/Hebrew using the wordless picture book, Frog, Where Are You? Narratives were transcribed and coded for frequency of ISTs and classified as perceptual, linguistic, motivational, linguistic, emotional, or physical. ISTs were also examined in terms of their role in microstructure (tokens and types) and macrostructure (Character, Setting, Initiating Event, Internal Response, Plan, Attempt, and Consequence) categories. In terms of microstructure, bilingual children with DLD produced fewer word types and tokens in general and fewer perceptual ISTs than bilingual TLD peers. In terms of macrostructure, children with DLD used six out of the seven story grammar elements in their narratives less than bilinguals with TLD. For ISTs and macrostructure, bilinguals with DLD produced fewer linguistic ISTs in Attempts than their peers with TLD. Bilingual children with DLD have difficulties in producing ISTs, and this difficulty is shown to be related to both microstructure and macrostructure abilities. Suggestions for clinical intervention are offered to assist children with DLD in producing narratives that involve mental state language related to characters' feelings, intentions, and goals.

Rethinking tokenization

Abstract Tokenization significantly influences language models (LMs)’ performance. This paper traces the evolution of tokenizers from word-level to subword-level, analyzing how they balance tokens and types to enhance model adaptability while controlling complexity. Despite subword tokenizers like Byte Pair Encoding (BPE) overcoming many word tokenizer limitations, they encounter difficulties in handling non-Latin languages and depend heavily on extensive training data and computational resources to grasp the nuances of multiword expressions (MWEs). This article argues that tokenizers, more than mere technical tools, should drawing inspiration from the cognitive science about human language processing. This study then introduces the “Principle of Least Effort” from cognitive science, that humans naturally seek to reduce cognitive effort, and discusses the benefits of this principle for tokenizer development. Based on this principle, the paper proposes that the Less-is-Better (LiB) model could be a new approach for LLM tokenizer. The LiB model can autonomously learn an integrated vocabulary consisting of subwords, words, and MWEs, which effectively reduces both the numbers of tokens and types. Comparative evaluations show that the LiB tokenizer outperforms existing word and BPE tokenizers, presenting an innovative method for tokenizer development, and hinting at the possibility of future cognitive science-based tokenizers being more efficient.

Knowledge-based Word Tokenization System for Urdu

Knowledge-based Word Tokenization System for Urdu

What Types of Vocabulary Do Students Learn? A Computer-assisted Study of EFL Textbooks

There has been an increase in research based on corpus analysis, especially in the field of teaching English to foreign language students. Such research covers aspects like grammar, syntax, morphology, semantics, and pragmatics in a broader sense. However, corpus studies on vocabulary are limited, and the need to work on them has increased in the last decade. Vocabulary is essential in learning and teaching any second language to English as a foreign language (EFL) classes. This study aims to thoroughly analyze vocabulary in three textbooks through a corpus analysis tool that is used to teach English as a foreign language to level 1, level 2, and diploma-taking students at a college in Saudi Arabia. Vocabulary is scrutinized through AntConC (1.4.0), a freeware program that uses statistical algorithms to provide visualizations of word frequencies, lexical clusters, and keywords. The results can be seen in the form of lists, tables, or charts to help you understand and draw conclusions. This selected corpus contains 163,716 word tokens. After statistical analysis of these words, English language teachers (ELTs) will understand what type of vocabulary is important for students for a particular level. It will also tell whether the vocabulary used in these textbooks is the correct representation of academic words when compared to baby BNC. Moreover, the study will provide insight to academicians, language teachers, and curriculum designers on what type of vocabulary is suitable for developing reading, writing, listening, or speaking skills according to the needs and levels of students.