General Text Research Articles

Exploration of designing an automatic classifier for questions containing code snippets-A case study of Oracle SQL certification exam questions.

This study uses the Oracle SQL certification exam questions to explore the design of automatic classifiers for exam questions containing code snippets. SQL's question classification assigns a class label in the exam topics to a question. With this classification, questions can be selected from the test bank according to the testing scope to assemble a more suitable test paper. Classifying questions containing code snippets is more challenging than classifying questions with general text descriptions. In this study, we use factorial experiments to identify the effects of the factors of the feature representation scheme and the machine learning method on the performance of the question classifiers. Our experiment results showed the classifier with the TF-IDF scheme and Logistics Regression model performed best in the weighted macro-average AUC and F1 performance indices. The classifier with TF-IDF and Support Vector Machine performed best in weighted macro-average Precision. Moreover, the feature representation scheme was the main factor affecting the classifier's performance, followed by the machine learning method, over all the performance indices.

Similarity Judul Tugas Akhir Menggunakan Metoda Cosine, Jaccard, Rabin-Karp Pada Jurusan TI

Plagiarism can occur anywhere and can be committed by anyone. For example, plagiarism by students when creating college assignments, where they unintentionally use someone else’s writing or ideas and forget to cite the source. Based on the above explanation, it is necessary to develop an information system to assess the percentage of plagiarism for proposed titles. After conducting an implementation test to measure the similarity accuracy and processing speed, a comparison was made between the proposed titles and the existing data, totaling 940 titles. The results showed a similarity rate of 84.13% with the Rabin-Karp method, 61.24% with the Cosine method, and 42.86% with the Jaccard method. In terms of processing speed, the Rabin-Karp method ranked first with a time of 0.5023 seconds, followed by the Cosine method with 0.5095 seconds, and the Jaccard method with 0.5103 seconds. From the test results, Jaccard Similarity is more suitable for short texts with unique word sets, but it is less precise for longer texts with important word distributions. Meanwhile, the Rabin-Karp method is not designed to measure general text similarity; instead, it is intended for fast and precise substring searches in pattern matching contexts

Representation of a fictional (artistic) world by a creative subject

The article is an overview of the current and controversial topic concerning the consideration of the author as a creative person who creates a fictional artistic world. The article is theoretical in nature, as it is directly devoted to describing the concept of text analysis with a focus of research attention on the study of a linguistic personality. The purpose of the work is to determine an approach to analyzing the processes of creating works of art and modeling fictional artistic worlds.The work rethinks and complements the concept of describing a linguistic personality proposed by Yu. Karaulov. The novelty of the work is determined by the shift in focus from attempts to interpret and describe the result of linguocreative activity to the study of the processes that determine it. The change in the subject in the paradigm of linguistic personality research allows us to expand the boundaries of the theory’s applicability, to formulate a more effective methodology in the interdisciplinary space, adapted to modern research into human consciousness.Further development of this approach to the text analysis and accumulation of empirical data in the future will allow formulating an adaptation theory of text, which is currently being developed by the research team. The results of this work have interdisciplinary potential for use not only in linguistics, general text theory, pragmatics, psycholinguistics, but also in the subject area of cognitive sciences.

TRANSITIVITY AND MOOD RESOURCES REALISING EXPERIENTIAL AND INTERPERSONAL MEANINGS IN “A TALE OF TWO CITIES”: A SYSTEMIC FUNCTIONAL LINGUISTICS ANALYSIS

This paper examines in depth how transitivity and mood resources are employed to construct experiential and interpersonal meanings in a literary text – a topic that has received limited attention in English literature teaching, learning and research, particularly at tertiary EFL (English as a Foreign Language) departments or faculties. The data for analysis is “Chapter 1 – “The Period” of “Book the First” of the three-book novel “A Tale of Two Cities” by the eminent British novelist Charles Dickens. The theoretical framework adopted for analysis is SFL (Systemic Functional Linguistics). The units of analysis are major clause simplexes. Findings of the study exhibit a number of noticeable transitivity and mood features employed in the text. In terms of transitivity resources, the writer represents experiential meanings in “The Period” by employing a very high frequency of material and relational processes, no behavioural process, all represented participants, a relatively small number of Circumstances, and a very high proportion of the past simple tense. As regards mood resources, the writer constructs interpersonal meanings in “The Period” by employing one hundred per cent of declarative mood, a predominant proportion of explicit Subjects, all non-interactive Subjects, and a predominant proportion of first participant Subjects. The study ends with a summary of the salient findings found in the text, a recommendation affirming the relevance of SFL as a theoretical framework for analysing and interpreting experiential and interpersonal meanings of texts in general and literary texts in particular for literature teaching, learning and research, and some suggestions for further study.

Thoughts on the Use the (Hebrew) Bible in the Assessment of Current Ethical Issues – Part II: The Question of the "How?"

In this study, the author looks at how biblical texts in general and Old Testament texts in particular can be used to address current ethical issues. The main focus is on the identification of the four main steps in the transfer from biblical text to current ethical issues: exegetical analysis of the biblical text – synthetical view of the biblical material – hermeneutical transfer – pragmatic application. In addition, some of the salient topics related to the ethical use of the (Hebrew) Bible are investigated: the traditional threefold division of the law, the special role of the Decalogue, the importance of creational order, the weight of exodus and Sinaitic covenant, the discussion about the unique position of love, and the lenses provided by the New Testament. Towards the end, guidelines for the assessment of the relative weight of rules and principles are presented.

Sentiment Analysis in Green Finance with LLMs

Green finance has gained global significance as governments and financial institutions emphasize sustainable investment. Understanding the sentiment of green finance reports can provide valuable insights into public perception, investor sentiment, and policy reception. This study uses three different models FinBERT, GPT-3.5 Turbo, and GPT-4o -- to perform sentiment analysis on over 1000 reports obtained from the International Finance Corporation (IFC) website. To assess the accuracy of the models, this paper manually labeled the sentiment of the reports into three categories: Positive, Negative, and Neutral. We compared the models outputs using standard metrics such as F1-score, Accuracy, Precision, and Recall. The findings indicate that GPT-3.5 Turbo outperforms the other models in terms of accuracy. GPT-4o shows superior performance compared to Finbert which trained on financial texts in extracting sentiment from general text. Even though FinBERT and GPT-4 have stronger financial text processing capabilities, GPT-3.5 Turbo can often capture the true intent and sentiment of the text more quickly and clearly, especially when trained on a relatively small text corpus. Its generalization and speed make it efficient for less complex financial tasks.

Personalized Patient Data and Behavioral Nudges to Improve Adherence to Chronic Cardiovascular Medications

Poor medication adherence is common. Text messaging is increasingly used to change patient behavior but often not rigorously tested. To compare different types of text messaging strategies with usual care to improve medication refill adherence among patients nonadherent to cardiovascular medications. Patient-level randomized pragmatic trial between October 2019 to April 2022 at 3 US health care systems, with last follow-up date of April 11, 2023. Adult (18 to <90 years) patients were eligible based on diagnosis of 1 or more cardiovascular condition(s) and prescribed medication to treat the condition. Patients who did not opt out and had a 7-day refill gap were randomized to 1 of 4 study groups. Generic text message refill reminders (generic reminder); behavioral nudge text refill reminders (behavioral nudge); behavioral nudge text refill reminders plus a fixed-message chatbot (behavioral nudge + chatbot); usual care. Primary outcome was refill adherence based on pharmacy data using proportion of days covered at 12 months. Secondary outcomes were clinical events of emergency department visits, hospitalizations, and mortality. Among 9501 enrolled patients, baseline characteristics across the 4 groups were comparable (mean age, 60 years; 47% female [n = 4351]; 16% Black [n = 1517]; 49% Hispanic [n = 4564]). At 12 months, the mean proportion of days covered was 62.0% for generic reminder, 62.3% for behavioral nudge, 63.0% for behavioral nudge + chatbot, and 60.6% for usual care (P = .06). In adjusted analysis, when compared with usual care, mean proportion of days covered was 2.2 percentage points (95% CI, 0.3-4.2; P = .02) higher for generic reminder, 2.0 percentage points (95% CI, 0.1-3.9; P = .04) higher for behavioral nudge, and 2.3 percentage points (95%, 0.4-4.2; P = .02) higher for behavioral nudge + chatbot, none of which were statistically significant after multiple comparisons correction. There were no differences in clinical events between study groups. Text message reminders targeting patients who delay refilling their cardiovascular medications did not improve medication adherence based on pharmacy refill data or reduce clinical events at 12 months. Poor medication adherence may be due to multiple factors. Future interventions may need to be designed to address the multiple factors influencing adherence. ClinicalTrials.gov Identifier: NCT03973931.

The Ideas of David Hume (1711-1776) in the Development of Psychology and Multicultural Education

Education needs a psychological approach to its process and mechanism. Contemporary psychology is rooted in philosophical thought and physiological inquiry. Psychology cannot be separated from the influence of three schools in philosophy, i.e., materialism, empiricism, and positivism. One of the empiricist thinkers who gave nuances and color in the development of psychology was David Hume (1711-1776). This article aims to reveal David Hume's thoughts, which provided motives in the development of psychology. This article is the result of research to develop a historical and philosophical theory of psychology. This research aims to investigate the background of the emergence of psychology from David Hume's thoughts. The focus of this research is to survey the influence of David Hume's empiricism on the development of psychology. The research method employed in this study is philosophical hermeneutics with synchronic and diachronic interpretations of David Hume's thoughts. As a qualitative study, the relevant data of the study was collected from various sources both primary and secondary. The primary literature used is texts on the history of psychology and David Hume’s works. The secondary sources for this research are various comments on Hume's thoughts and general psychology texts. The results of this research can be used as a reference in learning related to the history and philosophy of psychology. This article presents Hume's thoughts in the search for human nature along with the methodological principles he developed, which inspired scientists after Hume, as well as the influence of his thoughts on the progress of experimental psychology, which continues to develop, in giving a pattern to contemporary psychology

Clinical Prompt Learning With Frozen Language Models.

When the first transformer-based language models were published in the late 2010s, pretraining with general text and then fine-tuning the model on a task-specific dataset often achieved the state-of-the-art performance. However, more recent work suggests that for some tasks, directly prompting the pretrained model matches or surpasses fine-tuning in performance with few or no model parameter updates required. The use of prompts with language models for natural language processing (NLP) tasks is known as prompt learning. We investigated the viability of prompt learning on clinically meaningful decision tasks and directly compared this with more traditional fine-tuning methods. Results show that prompt learning methods were able to match or surpass the performance of traditional fine-tuning with up to 1000 times fewer trainable parameters, less training time, less training data, and lower computation resource requirements. We argue that these characteristics make prompt learning a very desirable alternative to traditional fine-tuning for clinical tasks, where the computational resources of public health providers are limited, and where data can often not be made available or not be used for fine-tuning due to patient privacy concerns. The complementary code to reproduce the experiments presented in this work can be found at https://github.com/NtaylorOX/Public_Clinical_Prompt.

The Music Humanities Significance of the Standard Pitch 432Hz

This study focuses on the socio-cultural narrative reflected in the standard pitch of 432Hz, establishes an argument for representing the Semantic structure of music humanities, which is the practical line of humanities, and critically reflects on the main character of humanities that can be accepted as a general education text, which is the concept of humanities as an educational ideology. The purpose is to expand the framework of humanistic thinking based on and interpretive performance. Accordingly, in order to explore the historical trend of the concept of standard pitch, we attempted to analyze literature focusing on the pattern of pitch levels in Western society from the late 14th century to today and the discussion of political, social, and cultural aspects related to this in the 19th and 20th centuries. The major issues in the background of pitch standardization and the musical humanities implications of the historical identity of 432Hz were discussed. 432Hz expanded the horizon of the humanities discourse process on the communication value contained in cultural diversity and presented another possibility to converge on a musical humanities trajectory, which sheds light on the value of cultural solidarity through consilient thinking, thus contributing to social It provides a foundation for understanding that can be transferred to the terrain. Accordingly, this study embraces the humanistic message reflected in the history of pitch standardization to share musical humanistic reasons as practical humanities with society in general, and to expand the dimension of cultural literacy to another level by reexamining the social function of music. We hope that it will be accepted as a general educational text.

Contrastive study of verbal structures in medical and general corpora in French

Summary This study examines the syntactic features of French medical and general texts to clarify their complexity and implications for comprehension. By analysing corpora from both domains, we found significant differences in the use of passive voice, present participles, negation and gerund constructions. In medical texts, the passive voice and present participles are more frequent, reflecting specialized discourse and precision. In contrast, negation and gerunds are more common in general texts, emphasizing the diversity of syntactic structures, and specific stylistic and argumentative effects. Our findings underline the need for clear communication in medical texts and provide empirical evidence for the simplification.

Enhanced Authorship Verification for Textual Similarity with Siamese Deep Learning

The internet is filled with documents written under false names or without revealing the author’s identity. Identifying the authorship of these documents can help decrease the success rate of potential criminals for financial or legal consequences. Most previous research on authorship verification focused on general text, but social media texts like tweets are more challenging since they are short, improperly structured, and cover a wide range of subjects. This paper proposes a new approach to determining textual similarity between these challenging messages. Inspired by the popularity of the Siamese networks in determining input similarity, four deep learning models based on this architecture were developed: a long-short-term memory (LSTM), a convolutional neural network (CNN), a combination of the two and a BERT model. These models were evaluated on a Twitter-based dataset, and the results show that the Siamese CNN-LSTM similarity model achieved the best performance with 0,97 accuracy.

FastComposer: Tuning-Free Multi-subject Image Generation with Localized Attention

AbstractDiffusion models excel at text-to-image generation, especially in subject-driven generation for personalized images. However, existing methods are inefficient due to the subject-specific fine-tuning, which is computationally intensive and hampers efficient deployment. Moreover, existing methods struggle with multi-subject generation as they often blend identity among subjects. We present FastComposer which enables efficient, personalized, multi-subject text-to-image generation without fine-tuning. FastComposer uses subject embeddings extracted by an image encoder to augment the generic text conditioning in diffusion models, enabling personalized image generation based on subject images and textual instructions with only forward passes. To address the identity blending problem in the multi-subject generation, FastComposer proposes cross-attention localization supervision during training, enforcing the attention of reference subjects localized to the correct regions in the target images. Naively conditioning on subject embeddings results in subject overfitting. FastComposer proposes delayed subject conditioning in the denoising step to maintain both identity and editability in subject-driven image generation. FastComposer generates images of multiple unseen individuals with different styles, actions, and contexts. It achieves 300$$\times $$ × –2500$$\times $$ × speedup compared to fine-tuning-based methods and requires zero extra storage for new subjects. FastComposer paves the way for efficient, personalized, and high-quality multi-subject image creation. Code, model, and dataset are available here (https://github.com/mit-han-lab/fastcomposer).

Pragmatic-Functional Importance of Translation of Titles and Headlines in Popular Science Texts

This article discusses the role and importance of titles and headlines in the structure of the general text, its pragmatic-functional tasks in ensuring the success of the text. The linguistic elements perform pragmatic function. The opinions of famous linguists on the titles and headlines, its structure and pragmatic functions are cited. Functions and syntactic models of titles and headlines are presented. Examples of popular science text titles, headlines and their translations into Uzbek and Russian are presented and analyzed.

Reviewer Experience Detecting and Judging Human Versus Artificial Intelligence Content: The Stroke Journal Essay Contest.

Artificial intelligence (AI) large language models (LLMs) now produce human-like general text and images. LLMs' ability to generate persuasive scientific essays that undergo evaluation under traditional peer review has not been systematically studied. To measure perceptions of quality and the nature of authorship, we conducted a competitive essay contest in 2024 with both human and AI participants. Human authors and 4 distinct LLMs generated essays on controversial topics in stroke care and outcomes research. A panel of Stroke Editorial Board members (mostly vascular neurologists), blinded to author identity and with varying levels of AI expertise, rated the essays for quality, persuasiveness, best in topic, and author type. Among 34 submissions (22 human and 12 LLM) scored by 38 reviewers, human and AI essays received mostly similar ratings, though AI essays were rated higher for composition quality. Author type was accurately identified only 50% of the time, with prior LLM experience associated with improved accuracy. In multivariable analyses adjusted for author attributes and essay quality, only persuasiveness was independently associated with odds of a reviewer assigning AI as author type (adjusted odds ratio, 1.53 [95% CI, 1.09-2.16]; P=0.01). In conclusion, a group of experienced editorial board members struggled to distinguish human versus AI authorship, with a bias against best in topic for essays judged to be AI generated. Scientific journals may benefit from educating reviewers on the types and uses of AI in scientific writing and developing thoughtful policies on the appropriate use of AI in authoring manuscripts.

No Consistent Evidence for Associations Between Various Forms of Social Media Usage and Emotional Prowess: A Multi-⁠Study Approach With Three Adult Samples

The good and bad impacts of social media on individuals and societies remain poorly understood and highly debated. An often-discussed, yet little-studied worry about social media usage is that it may breed diminished social and emotional abilities. Here, we tested this assumption across three studies with adult samples (N = 316, 1,879, 903). We used different indicators of emotional prowess (i.e., emotional intelligence, emotion recognition), a broad set of social media usage measures and adopted a three-pronged analysis approach featuring zero-order correlations, multiple regressions, and conditional random forests. Our findings do not support consistent evidence for associations between social media usage and emotional prowess. Instead, we find conflicting evidence for passive social media usage (related to lower overall emotional intelligence but better emotion recognition) and active social media usage (related to higher overall emotional intelligence but worse emotion recognition). We find some evidence for positive associations between emotional prowess and general smartphone usage and text messaging usage. Further, we find largely inconsistent and/or null effects for social media addiction, general social media usage, general smartphone usage, video gaming, and media sharing. In the absence of consistent effects of social media usage, we find strong, robust, and replicable associations between age and emotional prowess.

Presentation matters: Surface text features and text quality in written narratives of Dutch high school students with and without dyslexia.

Presentation features such as spelling, punctuation and handwriting can influence the evaluation of general text quality. High school students with dyslexia might therefore be at a disadvantage, as at least their spelling performance is typically poor(er). Furthermore, these students might show less sophisticated linguistic features of texts, such as word length and sentence complexity, that might also be related to text quality. We compared narratives written by Dutch high school students (mean age 13.7 years) with (n = 28) and without (n = 29) dyslexia. Students with dyslexia's texts contained more spelling errors and poorer handwriting quality, but not more punctuation errors. Teacher-rated general text quality was lower for the texts of students with dyslexia in uncorrected versions. When spelling and punctuation errors were corrected, no teacher-rated text quality differences emerged. No differences in linguistic text features were found. Furthermore, spelling, punctuation and, to a lesser extent, number of words per sentence clause were related to ratings of text quality across participants. These results confirm the influence of presentation features on text quality rating. They encourage teachers to be aware of this effect and emphasize the importance of spelling and writing support and interventions for students with dyslexia throughout education.

Integrated Perspectives on Clinical Decision Support: A Comparative Analysis of Knowledge Management Approaches.

We analyze five approaches to knowledge management in clinical decision support (CDS) systems: pattern recognition based on annotated imaging data, mining of stored structured medical data, text mining of published texts, computable knowledge design, and general or specific text corpora for large language models. Each method's strengths and limitations in automating clinical knowledge management while striving for a zero-error policy are evaluated, offering insights into their roles in enhancing healthcare through intelligent decision support. The study aims to inform decisions in the development of effective, transparent CDS systems in clinical and patient care settings.

Children’s understanding of referential nominal metaphors: a path to the heart of text comprehension

ABSTRACT A referential metaphor is a cohesive tie between lexical items that are metaphorically related, (e.g. “The seagull took the bread from the coffee table. No one heard the thief”). The reference from the thief back to the seagull is metaphorical because thieves are human. The present article presents arguments and some empirical evidence that resolution of referential metaphors shares several of the core processes of general text comprehension. Two studies are reported. The first study found that resolution of referential metaphors is closely correlated to text comprehension and that ability to resolve referential metaphors contributes unique variance to reading comprehension over and above resolution of literal references. The goal of the second study was to explore in depth the inferential processes involved in the resolution of referential metaphors. This second study assessed the relative contributions of text-based (“bridging”) and knowledge-based inferences to the resolution of referential metaphors. The results support the assumption that resolution of referential metaphors requires a combination of these two types of inferential processes in addition to other components that are shared with text comprehension. Together, the two studies indicate the processing of referential metaphors shares inferential processes with general reading comprehension and thus open new theoretical and practical perspectives.

Extracting method for fine-grained emotional features in videos

Multimodal Sentiment Analysis (MSA) has significant applications in social media analysis and healthcare. It utilizes features from multiple modalities e.g., video, general text, acoustics, and vision, to obtain more credible sentiment analysis results. However, previous studies have ignored substantial variations in sub-features emerging within both the acoustic and visual modalities during feature extraction. Instead, they primarily rely on a simple concatenation method to derive representations. Consequently, these approaches prevent feature extractors from effectively leveraging non-verbal (acoustic and visual) features, thereby, constraining the model’s overall performance. To solve this problem, this study proposes a method for extracting fine-grained emotional features from videos. By segregating the initial features of non-verbal modalities into distinct domains, then separately modeling and uniformly re-integrating them, our method effectively exploits the modality-specific information in these original features. Through extensive experiments, the performance of all models significantly improves using the features extracted following our method compared with original ones. This substantiates that our proposed approach more effectively utilizes features from non-verbal modalities compared with conventional approaches. This also underscores that processing non-verbal sub-features separately before integration represents a viable solution for enhancing the performance of the MSA model.