Distinct Words Research Articles

Playing with words: Comparing the vocabulary and lexical diversity of ChatGPT and humans

The introduction of Artificial Intelligence (AI) generative language models such as GPT (Generative Pre-trained Transformer) and conversational tools such as ChatGPT has triggered a revolution that can transform how text is generated. This has many implications, for example, as AI-generated text becomes a significant fraction of the text, would this affect the language capabilities of readers and also the training of newer AI tools? Would it affect the evolution of languages? Focusing on one specific aspect of the language: words; will the use of tools such as ChatGPT increase or reduce the vocabulary used or the lexical diversity? This has implications for words, as those not included in AI-generated content will tend to be less and less popular and may eventually be lost. In this work, we perform an initial comparison of the vocabulary and lexical diversity of ChatGPT and humans when performing the same tasks. In more detail, two datasets containing the answers to different types of questions answered by ChatGPT and humans, and a third dataset in which ChatGPT paraphrases sentences and questions are used. The analysis shows that ChatGPT-3.5 tends to use fewer distinct words and lower diversity than humans while ChatGPT-4 has a similar lexical diversity as humans and in some cases even larger. These results are very preliminary and additional datasets and ChatGPT configurations have to be evaluated to extract more general conclusions. Therefore, further research is needed to understand how the use of ChatGPT and more broadly generative AI tools will affect the vocabulary and lexical diversity in different types of text and languages.

Group-Based Distinctive Image Captioning with Memory Difference Encoding and Attention

AbstractRecent advances in image captioning have focused on enhancing accuracy by substantially increasing the dataset and model size. While conventional captioning models exhibit high performance on established metrics such as BLEU, CIDEr, and SPICE, the capability of captions to distinguish the target image from other similar images is under-explored. To generate distinctive captions, a few pioneers employed contrastive learning or re-weighted the ground-truth captions. However, these approaches often overlook the relationships among objects in a similar image group (e.g., items or properties within the same album or fine-grained events). In this paper, we introduce a novel approach to enhance the distinctiveness of image captions, namely Group-based Differential Distinctive Captioning Method, which visually compares each image with other images in one similar group and highlights the uniqueness of each image. In particular, we introduce a Group-based Differential Memory Attention (GDMA) module, designed to identify and emphasize object features in an image that are uniquely distinguishable within its image group, i.e., those exhibiting low similarity with objects in other images. This mechanism ensures that such unique object features are prioritized during caption generation for the image, thereby enhancing the distinctiveness of the resulting captions. To further refine this process, we select distinctive words from the ground-truth captions to guide both the language decoder and the GDMA module. Additionally, we propose a new evaluation metric, the Distinctive Word Rate (DisWordRate), to quantitatively assess caption distinctiveness. Quantitative results indicate that the proposed method significantly improves the distinctiveness of several baseline models, and achieves state-of-the-art performance on distinctiveness while not excessively sacrificing accuracy. Moreover, the results of our user study are consistent with the quantitative evaluation and demonstrate the rationality of the new metric DisWordRate.

Leveraging Contrastive Language–Image Pre-Training and Bidirectional Cross-attention for Multimodal Keyword Spotting

In resource-limited keyword spotting scenarios, the scarcity of annotated corpora hinders deep learning’s ability to develop robust models for representing acoustic features. Recent studies focus on contrastive learning, using paired examples for self-supervision, indicating a growing research interest in keyword spotting. Typically, models are trained on segmented spoken words. However, the absence of distinct word boundaries in lengthy audio poses significant challenges. This study presents a novel approach that integrates Contrastive Language–Image Pre-Training with Cross-Attention for Self-Supervised Alignment in the field of Multimodal Keyword Spotting. The proposed method introduces a cross-modal process for matching word pairs, enhancing collaborative effectiveness between audio and text embeddings, achieving word-level congruence across different modalities. Employing a self-supervised learning approach, we use a restricted number of annotated audio–text pairs to discern semantic congruities and divergences, resulting in improved multimodal feature representation. Using this strategy, we input into subsequent keyword detection tasks, adopting a Bidirectional Cross-Attention block for detailed coordination across modalities, bridging the semantic gap between diverse audio and text representations, consequently improving keyword localization precision. Comprehensive experiments conducted on the Aishell-2 and Librispeech datasets demonstrate that the proposed method markedly surpasses existing techniques, especially regarding the comprehensive performance metric F1 for keyword spotting. It attains state-of-the-art outcomes for both pristine and noisy speech, showing respective enhancements of 5.2% and 5.4% on Aishell-2, as well as 5.4% and 5.6% on Librispeech.

Themes and Language Style in Advertising Slogans of Vietnamese Banks and American Banks

Advertising slogans are important in branding companies and attracting customers. Businesses emphasize global thinking but tailor actions to local preferences, initiating advertisers to consider local culture when crafting advertising slogans. However, studies on advertising slogans are not abundant. This study used a qualitative approach to analyze and compare 50 Vietnamese and 69 American banks' advertising slogans in three aspects: themes, vocabulary, and syntax. Unlike previous multimodal studies on advertisements, this research highlighted the impact of culture on advertising, namely the influence of collectivism/individualism and high/low-context culture on advertising themes, vocabulary, and syntaxes. The study identified one similarity and three differences in the themes and pinpointed distinct words and syntax in these two groups of advertising slogans. Hopefully, the research will provide a deeper understanding of cultural influence on advertising so that advertising professionals can avoid potential cultural crashes in multi–national working environments.

WORD FORMATION THROUGH COMPOUNDING IN GEN-Z SLANG ON TIKTOK

Language is dynamic and reflective of societal changes, with Gen-Z, the first true digital natives, playing a pivotal role in linguistic evolution. This study examines the phenomenon of word formation through compounding in Gen-Z slang on TikTok, exploring its linguistic structures and meanings. By analyzing the common compounding patterns in Gen-Z slang, we gain insights into the creative processes and social influences shaping language use among this demographic. Utilizing a qualitative approach, data was collected from TikTok videos and comments between January 2023 and May 2024. The findings reveal 15 distinct compound words, with compound nouns being the most prevalent, followed by compound verbs and compound adjectives. Endocentric compounds were more common than exocentric ones. This research highlights the innovative nature of language within Generation Z, reflecting their values of self-expression, technological engagement, and creative problem-solving. The study underscores the role of social media in shaping contemporary language and provides a foundation for further exploration of linguistic innovations and their implications for language development and cultural expression in the digital age.

Dually-crosslinked ionic conductive hydrogels reinforced through biopolymer gellan gum for flexible sensors to monitor human activities

Human-machine interactions, monitoring of health equipment, and gentle robots all depend considerably on flexible strain sensors. However, making strain sensors have better mechanical behavior and an extensive sensing range remains an urgent difficulty. In this study, poly acrylamide-co-butyl acrylate with gellan gum (poly(AAm-co-BA)@GG) hydrophobic association networks and intermolecular hydrogen bonding interactions are used to fabricate dual cross-linked hydrogels for wearable resistive-type strain sensors. This could be an acceptable way to minimize the limitations in hydrogels previously identified. The robust fracture strength (870 kPa) and exceptional stretchability (1297 %) of the hydrogel arise from the collaborative action of intermolecular hydrogen bonding and hydrophobic associations. It also demonstrates exceptional resilience to repeated cycles of uninterrupted stretching and relaxation, retaining its structural integrity. The response and restoration times are 110 and 120 ms respectively. Furthermore, a wide sensing range (0–900 %), notable sensitivity across various strain levels, and an impressive gauge factor (GF) of 31.51 with high durability were observed by the dual cross-linked (DC) hydrogel-based strain sensors. The measured conductivity of the hydrogel was 0.32 S/m which is due to the incorporation of NaCl. Therefore, the hydrogels can be tailored to function as wearable strain sensors that can detect subtle human gestures like speech patterns, distinguish between distinct words, and recognize vibrations of the larynx during drinking, as well as large joint motions like wrist, finger, and elbow. Furthermore, these hydrogels are capable of reliably distinguishing and reproducing various printed text. These findings imply that any electronic device that demands strain-sensing functionality might make use of these developed materials.

Explainable text-tabular models for predicting mortality risk in companion animals

As interest in using machine learning models to support clinical decision-making increases, explainability is an unequivocal priority for clinicians, researchers and regulators to comprehend and trust their results. With many clinical datasets containing a range of modalities, from the free-text of clinician notes to structured tabular data entries, there is a need for frameworks capable of providing comprehensive explanation values across diverse modalities. Here, we present a multimodal masking framework to extend the reach of SHapley Additive exPlanations (SHAP) to text and tabular datasets to identify risk factors for companion animal mortality in first-opinion veterinary electronic health records (EHRs) from across the United Kingdom. The framework is designed to treat each modality consistently, ensuring uniform and consistent treatment of features and thereby fostering predictability in unimodal and multimodal contexts. We present five multimodality approaches, with the best-performing method utilising PetBERT, a language model pre-trained on a veterinary dataset. Utilising our framework, we shed light for the first time on the reasons each model makes its decision and identify the inclination of PetBERT towards a more pronounced engagement with free-text narratives compared to BERT-base’s predominant emphasis on tabular data. The investigation also explores the important features on a more granular level, identifying distinct words and phrases that substantially influenced an animal’s life status prediction. PetBERT showcased a heightened ability to grasp phrases associated with veterinary clinical nomenclature, signalling the productivity of additional pre-training of language models.

The Perceptions of Saudi EFL Students Toward the Effect of Anxiety on Their Oral Communication

Oral communication is one of the most important skills of communication among individuals with each other. It refers to communication between the students and their teachers through the spoken language, which includes a group of distinctive words, phrases, and sentences which are associated with specific meanings. However, there are many barriers that affect this communication. Therefore, this study addresses some of the barriers of anxiety when communicating using the English language in which it constitutes a major problem for EFL students and hinders their goals in learning the English language. This study aims to investigate the EFL students’ perceptions towards the effect of anxiety on their oral communication in various Saudi universities. Moreover, it explores some strategies that may help EFL students to overcome this problem. The research instrument used in this study was a questionnaire that was modified for the purposes of the study. The questionnaire consisted of 20 items divided into two parts. Furthermore, 102 EFL students participated in this study. The results showed that EFL students suffer from anxiety while speaking English, whether in a discussion class with the teacher or when presenting a presentation. The participants also agreed with most of the strategies stated in the survey. Based on the findings, recommendations and suggestions for further research are provided.

Endonormative stabilization in Philippine English lexis

AbstractIn the past 10 years, we have seen an enormous increase in the interest in Philippine English (PhilE) vocabulary. This is especially documented by the new entries of distinctive PhilE words in the Oxford English dictionary. Thanks to the rise of electronic mega‐corpora, such as GloWbE and the NOW Corpus, it has become possible to discover even more lexical innovations in PhilE. In this article, I compare nominal and adjectival suffixes across the following varieties of English: India, Singapore, Hong Kong, the Philippines, the United States, Canada, New Zealand and Great Britain. The comparisons were carried out on a quantitative and qualitative level, based on the findings in GloWbE, NOW and ICE. Apart from suffixes, the article also discusses less frequent and productive types of word‐formation, such as synthetic compounds and splinters (budgetarian).

Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations

Aspect-based sentiment analysis (ABSA), a fine-grained sentiment classification task, has received much attention recently. Many works investigate sentiment information through opinion words, such as "good'' and "bad''. However, implicit sentiment data widely exists in the ABSA dataset, whose sentiment polarity is hard to determine due to the lack of distinct opinion words. To deal with implicit sentiment, this paper proposes an ABSA method that integrates explicit sentiment augmentations (ABSA-ESA) to add more sentiment clues. We propose an ABSA-specific explicit sentiment generation method to create such augmentations. Specifically, we post-train T5 by rule-based data and employ three strategies to constrain the sentiment polarity and aspect term of the generated augmentations. We employ Syntax Distance Weighting and Unlikelihood Contrastive Regularization in the training procedure to guide the model to generate the explicit opinion words with the same polarity as the input sentence. Meanwhile, we utilize the Constrained Beam Search to ensure the augmentations are aspect-related. We test ABSA-ESA on two ABSA benchmarks. The results show that ABSA-ESA outperforms the SOTA baselines on implicit and explicit sentiment accuracy.

Distinctiveness, not dual coding, explains the picture-superiority effect.

The picture-superiority effect is the finding that memory for pictures exceeds memory for words on many tasks. According to dual-coding theory, the pictures' mnemonic advantage stems from their greater likelihood to be labelled relative to words being imaged. In contrast, distinctiveness accounts hold that the greater variability of pictures compared to words leads to their mnemonic advantage. Ensor, Surprenant, et al. tested these accounts in old/new and forced-choice recognition by increasing the physical distinctiveness of words and decreasing the physical distinctiveness of pictures. Half of the words were presented in standard black font, and half were presented in varying font styles, font sizes, font colours, and capitalisation patterns. Half of the pictures were presented in black and white and half in colour. Consistent with the physical-distinctiveness account but contrary to the dual-coding account, the picture-superiority effect was eliminated when comparing the black-and-white pictures to distinctive words. In the present study, we extend Ensor, Surprenant, et al.'s results to associative recognition and free recall. Results were consistent with physical distinctiveness. We argue that dual-coding theory is no longer a viable explanation of the picture-superiority effect.

An Acoustic Analysis of Description and Classification of Balti Segmental (Velar Sounds) Consonants

The goal of this study is to categorize solely the consonants (velars) of the Balti language, which is spoken in Pakistan's Baltistan area of Gilgit Baltistan. The Balti language was chosen for the study because it is one of the less well-known languages in Pakistan and because there has never been any substantial research done on the language's sounds or sound system.120 distinct words from various Balti novels were chosen in order to categorize Balti sounds. Using the program PRAAT, the acoustic analysis of Balti consonants has been examined to demonstrate their physical characteristics. In order to capture samples of various Balti consonant sounds, the investigators identified 20 indigenous Balti speakers, aged 16 to 35, enrolled in State College for Boys in Model Town and University of Technology and Management, Lahore, Pakistan. Spectrophotograms representing each sound were used to display the physical characteristics of the native Balti speakers' sound samples. The Balti language has ten fricative sounds, according to acoustic examinations of consonant sounds.

EVALUASI KEBUTUHAN SISWA SMA NEGERI BALUNG MASUK ESKTRAKURIKULER PENCAK SILAT NUR HARIAS

The martial art of Pencak Silat consists of two distinct words, namely "Pencak" and "Silat." In the Indonesian Dictionary (KBBI), "pencak" refers to a game (skill) used for self-defense through the expertise of parrying, evading, and so on. On the other hand, "silat" means martial arts/self-defense, engaging in combat. From these definitions, Pencak Silat can be succinctly understood as a self-defense skill that involves the expertise of parrying, evading, attacking, and similar techniques used in combat. The purpose of this research is to evaluate the needs of students at Balung State High School for participating in the extracurricular activity of Pencak Silat Nur Harias. This study employs a qualitative research method. The results of this research indicate that participating in the Pencak Silat extracurricular activity helps students in making new friends, boosting their self-confidence, and achieving accomplishments. Consequently, Pencak Silat is not just a martial art but also a means to cultivate positive character traits, appreciate local wisdom, and reinforce Indonesian cultural identity. Pencak Silat is a traditional Indonesian martial art with strong cultural and spiritual values. Its goal is to defend, preserve, and attain life's harmony in order to enhance faith and devotion. In the learning process, the benefits encompass cognitive, affective, and psychomotor development. Pencak Silat training enhances quick thinking, sportsmanship, and motor skills. Besides being a sport, Pencak Silat also contributes to strengthening Indonesian cultural identity and fostering positive character traits among its practitioners.

Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies

In the rapidly advancing era of educational technology, customized learning materials have the potential to enhance individuals’ learning capacities. This research endeavors to devise an effective method for detecting a learner’s preferred learning style and subsequently adapting the learning content to align with that style, utilizing artificial intelligence AI techniques. Our investigation finds that analyzing learners’ web tracking logs for activity classification and categorizing individual responses for feedback classification are highly effective methods for identifying a learner’s learning styles, such as visual, auditory, and kinesthetic. A custom dataset has been constructed in this research comprising approximately 506 samples and 22 features utilizing the Moodle learning management system (LMS), successfully categorizing students into their respective learning styles. Furthermore, decision tree, random forest, support vector machine (SVM), logistic regression, XGBoost, blending ensemble, and convolutional neural network (CNN) algorithms with corresponding optimized hyperparameters and synthetic minority oversampling technique (SMOTE) have been applied for learning behavior classification. The blending ensemble technique with the XGBoost meta-learning model accomplished the best performance for learning style detection with an accuracy of 97.56%. Next, the text content of the electronic documents is modified by employing different natural language processing (NLP) techniques, including named entity recognition of spaCy, knowledge graph, generative pre-trained transformer 3 (GPT-3), and text-to-text transfer transformer (T5) model, to accommodate diverse learning styles. Various approaches, such as color coding, audio scripts, mind maps, flashcards, etc., are implemented to adapt the content effectively for the detected categories of learners. The spaCy NLP-based named entity recognition (NER) model demonstrates a 94.16% F1 score and 0.92 exact match ratio for color coding text generation of ten electronic documents comprising 790 distinct individual words. These modifications aim to cater to the unique preferences of learners, fostering a more personalized and engaging educational experience. To the best of our knowledge, this is the first time an integrated learning style detection and content modification system has been developed in this work utilizing efficient AI techniques and a private dataset.

Machine Learning-Based Prediction Models for Cognitive Decline Progression: A Comparative Study in Multilingual Settings Using Speech Analysis.

Mild cognitive impairment (MCI) is a condition commonly associated with dementia. Therefore, early prediction of progression from MCI to dementia is essential for preventing or alleviating cognitive decline. Given that dementia affects cognitive functions like language and speech, detecting disease progression through speech analysis can provide a cost-effective solution for patients and caregivers. In our study, we examined spontaneous speech (SS) and written Mini Mental Status Examination (MMSE) scores from a 60-patient dataset obtained from the Mugla University Dementia Outpatient Clinic (MUDC) and a 153-patient dataset from the Alzheimer's Dementia Recognition through Spontaneous Speech (ADRess) challenge. Our study, for the first time, analyzed the impact of audio features extracted from SS in distinguishing between different degrees of cognitive impairment using both an Indo-European language and a Turkic language, which exhibit distinct word order, agglutination, noun cases, and grammatical markers. When each machine learning model was tested on its respective trained language, we attained a 95% accuracy using the random forest classifier on the ADRess dataset and a 94% accuracy on the MUDC dataset employing the multilayer perceptron (MLP) neural network algorithm. In our second experiment, we evaluated the effectiveness of each language-specific machine learning model on the dataset of the other language. We achieved accuracies of 72% for English and 76% for Turkish, respectively. These findings underscore the cross-language potential of audio features for automated tracking of cognitive impairment progression in MCI patients, offering a convenient and cost-effective option for clinicians or patients.

Handwritten Vedic Sanskrit Text Recognition Using Deep Learning and Convolutional Neural Networks

Recognizing Vedic Sanskrit text is essential for accessing classical Indo-Aryan language, predominantly utilized in the Vedas. Currently, there is limited awareness about the Vedas, making this field a highly demanding and challenging area in pattern recognition. To accelerate progress in optical character recognition (OCR), deep learning methods are indispensable. This article presents a novel approach to Vedic Sanskrit text recognition, incorporating deep convolutional architectures with their respective interpretations. We introduce three modified 4-fold CNN architectures and the AlexNet model. Our system comprises a handwritten dataset containing 140 distinct Vedic Sanskrit words, with approximately 500 images per word, totaling around 70,000 images. The dataset is partitioned for training and testing in an 80:20 ratio. Training is conducted using 20% of the samples, and the resulting model is applied to the deep convolutional network with varied sets of neurons in their hidden layers. Our proposed method demonstrates robust support for accurate Vedic Sanskrit word classification. The recognition rate achieved in our research is 97.42%, with an average recognition time of 0.3640 milliseconds, surpassing existing CNN-based approaches.

Conceptual anatomy of the female genitalia using text mining and implications for patient care

This article analyses the conceptual histories of words associated with female genital parts to explore how they may affect the lived experience of people with these parts and the quality...

Chinese readers utilize emotion information for word segmentation.

We reported a large-scale Internet-based experiment to investigate the impact of emotion information on Chinese word segmentation, in which participants completed an overlapping ambiguous string (OAS) segmentation task and the Chinese version of Beck Depression Inventory-II in a counterbalanced order. OAS is a three-character string (ABC) in which the middle character can form a distinct word with both the character on its left side (word AB) and the character on its right side (word BC). Participants were presented with isolated OASs and were asked to report the word they identified first. Emotional OAS was constructed by a combination of a neutral word and an emotional word, with the neutral and emotional words sharing character B. We orthogonally manipulated the valence of the emotional words (positive vs. negative) and their position in the OAS (left-side vs. right-side). The results showed that compared with neutral words, both positive and negative words were more likely to be segmented, and this segmentation outcome was not affected by readers with different depression tendencies. These findings suggest that emotion information can influence word segmentation, and that both positive and negative words take precedence over neutral words in the word segmentation process. This study provides a new perspective and evidence to understand the impact of emotion information on word processing.

Repurposing verbal fluency: Word‐property analysis reveals neurocognitive markers of Hispanics with Alzheimer’s disease and mild cognitive impairment

AbstractBackgroundThough central to dementia assessments, verbal fluency tasks are suboptimally exploited. Performance is typically measured by counting valid responses. This precludes insights on which word types are least accessible to patients, while failing to discriminate among disorders. Alternatively, each word can be decomposed into relevant properties that drive semantic memory retrievability. Recently, our team has combined objective lexical measures with multimodal imaging to examine whether this approach is sensitive to Hispanics with Alzheimer’s disease (AD) and mild cognitive impairment (MCI), vis‐à‐vis others with behavioral variant frontotemporal dementia (bvFTD).MethodIn Experiment‐1, we recruited 32 AD patients and 32 bvFTD patients, matched with 27 HCs. Experiment‐2 involved 60 persons with MCI and 60 matched HCs. Participants performed phonemic and semantic fluency tasks. We counted valid responses and, using normative databases, extracted quantitative data of six word properties: frequency (occurrences in everyday use), granularity (conceptual specificity), neighborhood (words with similar phonological structure), length (number of syllables), familiarity (subjective recognizability), and imageability (ease of mental visualization). We examined whether these variables were robust to discriminate between groups (via ANOVAs), identify individuals from each cohort (via linear regressions), and capture cognitive, neuroanatomical, and functional connectivity disruptions.ResultsIn Experiment‐1, while both patient groups produced fewer valid responses than HCs, only AD patients showed distinct word properties (higher frequency, lower granularity, more phonological neighbors). Multivariate word properties robustly identified individual AD (but not bvFTD) patients from HCs (AUC = .89), driven by the abovementioned properties. These features correlated with executive outcomes only in AD, and they predicted patients’ anatomical and functional disruptions along temporal, frontal, and cingulate regions. In Experiment‐2, the same word properties differentiated MCI individuals from HCs, with multivariate classification reaching an AUC of .75. These features correlated with the volume of, and connectivity between, temporal and frontal regions that partly overlapped with those observed in AD.ConclusionVerbal fluency tasks can be repurposed to reveal condition‐specific markers of persons with and at increased risk for AD. New avenues emerge towards scalable, low‐cost assessment of Hispanics and, potentially, other underrepresented populations.

Probing Lexical Ambiguity in Chinese Characters via Their Word Formations: Convergence of Perceived and Computed Metrics.

Lexical ambiguity is pervasive in language, and the nature of the representations of an ambiguous word's multiple meanings is yet to be fully understood. With a special focus on Chinese characters, the present study first established that native speaker's perception about a character's number of meanings was heavily influenced by the availability of its distinct word formations, while whether these meanings would be perceived to be closely related was driven by further conceptual analysis. These notions were operationalized as two computed metrics, which assessed the degree of dispersion across individual word formations and the degree of propinquity across clusters of word formations, respectively, in a distributional semantic space. The observed correlations between the computed and the perceived metrics indicated that the utility of word formations to tap into meaning representations of Chinese characters was indeed cognitively plausible. The results have demonstrated the extent to which distributional semantics could inform about meaning representations of Chinese characters, which has theoretical implications for the representation of ambiguous words more generally.