Language Text Research Articles

Language bursts and text quality in digital writing by young EFL learners

This study investigates the relationship between pausing and revision behaviors, as captured by pause bursts (P-bursts) and revision bursts (R-bursts), and text quality in digital L2 writing contexts among young English as a Foreign Language (EFL) learners. The study involved 22 elementary school students aged 10–11 years, with English proficiency levels ranging from A1 to A2 (CEFRL). Participants completed a picture description writing task, and their writing processes were recorded using keystroke logging software. The results revealed that frequent pauses (P-bursts) were negatively correlated with text quality, suggesting cognitive overload or difficulties in language processing. In contrast, revision bursts (R-bursts) exhibited a positive correlation with text quality, indicating that the revision process played a crucial role in refining and improving the written output. Regression analysis further demonstrated that R-bursts significantly predicted text quality, underscoring the importance of revision behavior among young L2 writers. These findings contribute to our understanding of the cognitive processes underlying L2 writing in digital contexts and highlight the central role of revisions in enhancing text quality among young EFL learners. Educational implications emphasize the need for instructional practices that encourage and support the revision process in writing instruction for this population.

Emotion Detection inArabic Text in Social Media: A Brief Survey

Emotion detection in Arabic text in social media has gained significant attention due to the increasing use of social media platforms in the Arab world. It is a special case of affective computing that relies on natural language processing techniques to recognize emotions from written text. This article provides a comprehensive review of different approaches used in developing models for emotion detection in Arab textual data in social media. Social media have a substantial impact on societies and have contributed significantly to many fields (marketing, recommendation systems, etc.). Arabic language texts on social networking sites in all their forms (posts -comments -messages -any other form) express the personal feelings of the person concerned; if they are understood correctly, the understanding of the written text will be correct. Scientific research has recently increased in this field to determine people's feelings through their writing of texts. In addition, we have highlighted the utilization of deep learning architectures in the improvement of the performances of models for different types of deep learning models.

Information extraction from medical case reports using OpenAI InstructGPT

Background and objectiveResearchers commonly use automated solutions such as Natural Language Processing (NLP) systems to extract clinical information from large volumes of unstructured data. However, clinical text's poor semantic structure and domain-specific vocabulary can make it challenging to develop a one-size-fits-all solution. Large Language Models (LLMs), such as OpenAI's Generative Pre-Trained Transformer 3 (GPT-3), offer a promising solution for capturing and standardizing unstructured clinical information. This study evaluated the performance of InstructGPT, a family of models derived from LLM GPT-3, to extract relevant patient information from medical case reports and discussed the advantages and disadvantages of LLMs versus dedicated NLP methods. MethodsIn this paper, 208 articles related to case reports of foreign body injuries in children were identified by searching PubMed, Scopus, and Web of Science. A reviewer manually extracted information on sex, age, the object that caused the injury, and the injured body part for each patient to build a gold standard to compare the performance of InstructGPT. ResultsInstructGPT achieved high accuracy in classifying the sex, age, object and body part involved in the injury, with 94%, 82%, 94% and 89%, respectively. When excluding articles for which InstructGPT could not retrieve any information, the accuracy for determining the child's sex and age improved to 97%, and the accuracy for identifying the injured body part improved to 93%. InstructGPT was also able to extract information from non-English language articles. ConclusionsThe study highlights that LLMs have the potential to eliminate the necessity for task-specific training (zero-shot extraction), allowing the retrieval of clinical information from unstructured natural language text, particularly from published scientific literature like case reports, by directly utilizing the PDF file of the article without any pre-processing and without requiring any technical expertise in NLP or Machine Learning. The diverse nature of the corpus, which includes articles written in languages other than English, some of which contain a wide range of clinical details while others lack information, adds to the strength of the study.

Optical Character Recognition of Balochi Script

Optical Character Recognition is considered one of the fastest methods of data entry. OCR converts the text image representation of x and y coordinates representing pixel information to be converted into text data in a particular language. OCR as a field of pattern recognition and document image understanding, OCR requires a challenging job once a different language text is available on the image. Difference in language script will pose different challenges for OCR which requires entirely different approaches and algorithms. Latin scripts require a different approach whereas the Balochi adopted language scripts require a different approach. In this regard, various solutions have been proposed for different languages. Segmentation is considered one of the important tasks in the process of OCR. A good segmentation will definitely increase the accuracy of an OCR. Segmentation includes the segmentation of text lines from text images which are further divided into words. These segmented words are further divided into characters which are to be recognized. A single segmentation algorithm to segment various scripts of the languages is proposed in this study which checks the script and then segments the text image for the further processing in OCR. The proposed generalized algorithm will check the style, direction and other properties of the script and then adopts the segmentation process to segment text lines, words and characters of the language. The proposed algorithm segments more than ten languages of three scripts and segments for their OCRs. These images can be further processed for feature extraction and classification further. The process of OCR for selected languages will be made easier to recognize. Multiple scripts, languages and images were experimented, and the proposed algorithm successfully segmented 42,833 images of text line, words and character image. The algorithm provides 97% accuracy while segmenting these images and can be extended to further languages as well as scripts .

LINGUISTIC AESTHETISM IN NEOREALIST UKRAINIAN FICTION OF THE BEGINNING OF THE XX CENTURY

The purpose of the article is to analyze the linguistic aestheticism of neorealist works of art of the beginning of the ХХ century. Attention is focused on certain tasks: to determine the positions of neorealism in Ukrainian literature; to prove the legality of consideration of linguistic aestheticism in neorealist works; find out the linguistic specificity of neorealism; to reveal the features of neorealist aestheticism in the language of the artistic text. The works of Ukrainian artists and scientists of the ХХ century were chosen as the object of research. The subject of the study is to find out the specifics of the linguistic and aesthetic-worldview foundations of neorealism. The theoretical and methodological principles of the research are based on some concepts and ideas of domestic philologist scientists O. Potebny, O. Biletskyi, L. Bulakhovskyi, D. Chyzhevskyi, I. Franko, Lesya Ukrainka, M. Zerov, M. Kodak, V. Lesyn and O. Pulynets, V. Fashchenko and others. The work uses comparative, descriptive, comparative, and structural research methods. The scientific novelty of the work lies in the fact that, for the first time, attention is drawn to the linguistic aestheticism of neorealist works of Ukrainian authors using the examples of linguistic and stylistic concepts of well-known linguists, literary critics, and artists. The paper offers a review of theoretical explorations and positions of well-known domestic scientists on linguistic aestheticism in neorealist Ukrainian fiction of the beginning of the ХХ century. Based on these considerations, one can turn to the artistic word in the author’s text, which reveals the unique beauty and mystery of human life, movement in the world, the author’s worldview and worldview through the magic of words. The prospects of researching the linguistic aestheticism of neorealist works of art of the beginning of the ХХ century provide an opportunity for the development of scientific horizons and experimental freedom in investigative creativity.

LLM-Powered Natural Language Text Processing for Ontology Enrichment

LLM-Powered Natural Language Text Processing for Ontology Enrichment

Adaptive weighted feature fusion for multiscale atrous convolution‐based 1DCNN with dilated LSTM‐aided fake news detection using regional language text information

AbstractThe people in the world rely on social media for gathering news, and it is mainly because of the development of technology. The approaches employed in natural language processing are still deficient in judgement factors, and these techniques frequently rely upon political or social circumstances. Numerous low‐level communities in the area are curious after experiencing the negative effects caused by the spread of false information in different sectors. Low‐resource languages are still distracted, because these techniques are extensively employed in the English language. This work aims to provide an analysis of regional language fake news and develop a referral system with advanced techniques to identify fake news in Hindi and Tamil. This proposed model includes (a) Regional Language Text Collection; (b) Text preprocessing; (c) Feature Extraction; (d) Weighted Stacked Feature Fusion; and (e) Fake News Detection. The text data is collected from the standard datasets. The collected text data is preprocessed and given into the feature extraction, which is done by using bidirectional encoder representations from transformers (BERT), transformer networks, and seq2seq network for extracting the three sets of language text features. These extracted feature sets are inserted into the weighted stacked feature fusion model, where the three sets of extracted features are integrated with the optimized weights that are acquired through the enhanced osprey optimization algorithm (EOOA). Finally, these resultant features are given to multi‐scale atrous convolution‐based one‐dimensional convolutional neural network with dilated long short‐term memory (MACNN‐DLSTM) for detecting the fake news. Throughout the result analysis, the experimentation is conducted based on the standard Tamil and Hindi datasets. Moreover, the developed model shows 92% for Hindi datasets and 96% for Tamil datasets which shows effective performance regarding accuracy measures. The experimental analysis is carried out by comparing with the conventional algorithms and detection techniques to showcase the efficiency of the developed regional language‐based fake news detection model.

Evaluating Generative Artificial Intelligence on Multilingual Sentiment Analysis

This study shows that natural language processing (NLP), large language models (LLM), and generative Artificial Intelligence are becoming the most important. LLM is considered essential for many NLP tasks, including question-answering, classification, interpretation, and writing. New LLMs must be able to analyze and create documents in different languages as they know how to train training materials in different languages such as ChatGPT, BLOOMZ, and others. Considering how frequently LLMs are used, it is critical to assess the effectiveness in multi-lingual environments. In a zero-shot context, the present generative models are still useful in producing text in Indian languages. On the other hand, generative models routinely outperform human quality-based evaluation when it comes to English language generation and Indian languages. LLMs are not meant to be used in a zero-shot manner in downstream applications due to poor generating performance.

Effectiveness Effectiveness of Wearable Electronic Device for Fall Risk Prevention in Community Elderly: A Literature Review

The increasing incidence of falls in the elderly has become a global health issue. The development of wearable electronic device technology is able to monitor daily activities, measure step counts, physical activity levels, and even sleep patterns of the elderly, while providing early warnings when changes occur that may increase the risk of falls. The aim is to see the effectiveness of using wearable electronic devices in preventing the risk of falls in the elderly in the community. PICO in this study is Population: Community-dwelling elderly, Intervention: wearable electronic device, Comparisons: none, and Outcome: fall prevention. Researchers used 3 search data sources, namely Embase, Pubmed, and Science Direct. The inclusion and exclusion criteria of this study are literature published in electronic data sources from 2018-2023, is a type of quantitative research, the elderly population in the community, English language and available in full text. This literature review includes ten articles that discuss the utilization of wearable electronic device technology, the types of wearable devices used include Inertial Measurement Units (IMU), biosensors, and accelerometers to evaluate the mobility, gait, and balance of the elderly. This literature review shows that wearable electronic device technology can effectively detect falls and motorize the incidence of falls in the elderly

UVLHub: A feature model data repository using UVL and open science principles

Feature models are the de facto standard for modelling variabilities and commonalities in features and relationships in software product lines. They are the base artefacts in many engineering activities, such as product configuration, derivation, or testing. Concrete models in different domains exist; however, many are in private or sparse repositories or belong to discontinued projects. The dispersion of knowledge of feature models hinders the study and reuse of these artefacts in different studies. The Universal Variability Language (UVL) is a community effort textual feature model language that promotes a common way of serializing feature models independently of concrete tools. Open science principles promote transparency, accessibility, and collaboration in scientific research. Although some attempts exist to promote feature model sharing, the existing solutions lack open science principles by design. In addition, existing and public feature models are described using formats not always supported by current tools. This paper presents ▪ , a repository of feature models in UVL format. ▪ provides a front end that facilitates the search, upload, storage, and management of feature model datasets, improving the capabilities of discontinued proposals. Furthermore, the tool communicates with Zenodo – one of the most well-known open science repositories – providing a permanent save of datasets and following open science principles. ▪ includes existing datasets and is readily available to include new data and functionalities in the future. It is maintained by three active universities in variability modelling.

Human Versus Machine Intelligence: Assessing Natural Language Generation Models Through Complex Systems Theory.

The introduction of Transformer architectures - with the self-attention mechanism - in automatic Natural Language Generation (NLG) is a breakthrough in solving general task-oriented problems, such as the simple production of long text excerpts that resemble ones written by humans. While the performance of GPT-X architectures is there for all to see, many efforts are underway to penetrate the secrets of these black-boxes in terms of intelligent information processing whose output statistical distributions resemble that of natural language. In this work, through the complexity science framework, a comparative study of the stochastic processes underlying the texts produced by the English version of GPT-2 with respect to texts produced by human beings, notably novels in English and programming codes, is offered. The investigation, of a methodological nature, consists first of all of an analysis phase in which the Multifractal Detrended Fluctuation Analysis and the Recurrence Quantification Analysis - together with Zipf's law and approximate entropy - are adopted to characterize long-term correlations, regularities and recurrences in human and machine-produced texts. Results show several peculiarities and trends in terms of long-range correlations and recurrences in the last case. The synthesis phase, on the other hand, uses the complexity measures to build synthetic text descriptors - hence a suitable text embedding - which serve to constitute the features for feeding a machine learning system designed to operate feature selection through an evolutionary technique. Using multivariate analysis, it is then shown the grouping tendency of the three analyzed text types, allowing to place GTP-2 texts in between natural language texts and computer codes. Similarly, the classification task demonstrates that, given the high accuracy obtained in the automatic discrimination of text classes, the proposed set of complexity measures is highly informative. These interesting results allow us to add another piece to the theoretical understanding of the surprising results obtained by NLG systems based on deep learning and let us to improve the design of new informetrics or text mining systems for text classification, fake news detection, or even plagiarism detection.

PENGGUNAAN METODE JIGSAW DALAM PEMBELAJARAN MATA KULIAH TRANSLATION UNTUK MENGHASILKAN KEEFEKTIFAN DALAM MENYELESAIKAN TERJEMAHAN

Translation learning is a complex process that requires a profound understanding of both the source and target languages. This study aims to explore the use of the Jigsaw method in the context of translation course learning, with a focus on enhancing students' effectiveness in completing translations. The Jigsaw method is a cooperative learning approach where students work in small groups to understand parts of the material and then take responsibility for teaching that material to other group members. This study integrates the Jigsaw method into translation learning by utilizing complex and varied source language texts. Students are divided into small groups and assigned tasks to study specific sections of the text to be translated. After thoroughly understanding their respective sections, they regroup with other members who have studied different parts. Through collaboration and discussion, students exchange information and discuss translation strategies. The results of this study indicate that the use of the Jigsaw method significantly enhances students' effectiveness in completing translations. Collaboration among students allows for the exchange of ideas and strategies that can improve their understanding of the source text and produce more accurate and detailed translations. Additionally, through this cooperative learning process, students also develop important social and communication skills essential in the translation profession.

Analysis of methods for training domain-specific language models in the area of legal contracts generation

The subject of the research is machine learning models and methods for generating legal contracts with limited resources and performance evaluation benchmarks. The goal of the work is to analyse approaches of domain-specific Large Language Models development and to find the optimal method of creating independent specialized systems that can generate contracts in different languages and legal systems. The article addresses the following tasks: identification of existing companies and solutions in this area, exploring approaches to create texts in natural language, analysis of evaluation and comparison methods of such systems, inspecting limitations and shortcomings of existing solutions and approaches, finding the optimal method of developing systems with limited resources. The following results were obtained: approaches of natural language generation and their features were investigated; the "Transformer" architecture was defined as a modern standard in the field of text information generation; different model types which are based on this architecture were considered; data sources for training were analysed; methods of adapting models in specialized areas were considered; model evaluating benchmarks for various tasks were reviewed; shortcomings of the existing specialized language models and the incompleteness of existing benchmarks for contract generation task evaluation were revealed. As a result of the analytical experiment, it was determined that the Retrieval-Augmented Generation method is the most optimal for solving the given task under the given conditions. The conducted experiment and its results can be used as a basis for further research of domain-specific language models development with limited resources. Conclusions: the article provides an overview of natural language generation methods using modern machine learning techniques, considers their advantages and disadvantages for small companies and scientific institutions that have limited resources. The work examinates a specialized legal domain and the problem of contract generation and determines the most optimal method to solve it.

Geographical and linguistic perspectives on developing geoparsers with generic resources

Geoparsers aim to find place names in unstructured texts and locate them geographically. This process produces georeferenced data usable for spatial analyses or visualisations. Much geoparsing research and development has thus far focused on the English language, yet languages are not alike. Geoparsing them may necessitate language-specific processing steps or data for training geoparsing systems. In this article, we applied generic language and GIS resources to geoparsing Finnish texts. We argue that using generic resources can ease the development of geoparsers, and free up resources to other tasks, such as annotating evaluation corpora. A quantitative evaluation on new human-annotated news and tweet corpora indicates robust overall performance. A systematic analysis of the geoparser output reveals errors and their causes at each processing step. Some of the causes are specific to Finnish, and offer insights to geoparsing other morphologically complex languages as well. Our results highlight how the language of the input text affects geoparsing. Additionally, we argue that toponym resolution metrics based on error distance have limitations, and proposed metrics based on spatial intersection with ground-truth polygons.

FEATURES OF FUNCTIONING OF EXPRESSIVE REDUCED VOCABULARY IN CONTEMPORARY FICTION TEXTS

This article is devoted to the consideration of the category of expressiveness and its embodiment in literary language and art text. Expressive lexical units belonging to the reduced lexicon in fiction works of the first quarter of the 21st century (“Textˮ by Dmitry Glukhovsky, “Earthˮ by Mikhail Elizarov and “Hijackingˮ by Danil Korecki) are singled out and their functions are determined. The analysis is carried out on the material of various lexicographic editions, among which both explanatory dictionaries and highly specialised dictionaries (dictionaries of argot, jargon) are used. The most common functions that were identified in the context of the named works are the following: characterisation of the linguistic picture of the world of the hero and the author, the “anti-aestheticˮ artistic picture of the world of the author (author’s intention) and a means of characterisation of the character – speech or author’s when using non- self-directed speech. It is indicative that some functions can be combined in the text, which is confirmed by the examples from contemporary works of fiction given in the article. It is concluded that the use of reduced expressive vocabulary in the modern fiction text demonstrates both general linguistic processes and methods of creating artistic imagery, the specificity of which is associated with a kind of “anti-aestheticˮ.

About the use of machine learning in modeling business processes

In a highly competitive environment, as well as the activation of the domestic production market, enterprises need to quickly adapt to modern conditions. There is an obvious increase in the number of small manufacturing enterprises that participate in tenders on electronic trading platforms and offer their services to large enterprises, especially this growth is noticeable in the field of militaryindustrial complex. Customers prefer to cooperate with small enterprises that are adaptable to the order conditions and have not only short terms of order fulfillment, but also a flexible pricing system due to low administrative and bureaucratic costs. At the same time, such enterprises have problems with the organization of business processes when the volume of orders grows. In this paper, the authors have built a model of the Quality Control process using BPMN method on the basis of small enterprise practice. This model can be the basis for training a machine learning system to build a model of business processes. Natural language text processing is proposed as an area of artificial intelligence, which allows enterprises to use this unified technology to reduce the cost of developing and describing business processes.

Leveraging LangChain agents to automate data analysis for SaaS.

This paper investigates the implementation of LangChain, a language model-powered framework, in automating data analysis within the SaaS sector.The approach included setting up LangChain agents for exploratory, univariate, and bivariate analyses, as well as hypothesis testing, transforming extensive data into human language text answers. Experiments confirmed the effectiveness of the proposed method using GPT-3.5 LLM agents, tested on the Amazon AWS SaaS Sales Dataset. Identified deficiencies need to be addressed for complex queries and comprehensive reports. Future research prospects include improving the method for complex queries, providing more detailed information about companies and business models, creating report templates, and training the model to solve complex questions. To automate data analysis, the method of using LangChain agents was proposed. A software implementation was developed, and data analysis indicators were studied using SaaS sales data as a case study. The study demonstrated LangChain agents’ capability to automate data analysis processes in the SaaS industry. Future research will aim to expand its application across more complex data, larger number of data questions, and pre-trained LLMs

MedT5SQL: a transformers-based large language model for text-to-SQL conversion in the healthcare domain.

In response to the increasing prevalence of electronic medical records (EMRs) stored in databases, healthcare staff are encountering difficulties retrieving these records due to their limited technical expertise in database operations. As these records are crucial for delivering appropriate medical care, there is a need for an accessible method for healthcare staff to access EMRs. To address this, natural language processing (NLP) for Text-to-SQL has emerged as a solution, enabling non-technical users to generate SQL queries using natural language text. This research assesses existing work on Text-to-SQL conversion and proposes the MedT5SQL model specifically designed for EMR retrieval. The proposed model utilizes the Text-to-Text Transfer Transformer (T5) model, a Large Language Model (LLM) commonly used in various text-based NLP tasks. The model is fine-tuned on the MIMICSQL dataset, the first Text-to-SQL dataset for the healthcare domain. Performance evaluation involves benchmarking the MedT5SQL model on two optimizers, varying numbers of training epochs, and using two datasets, MIMICSQL and WikiSQL. For MIMICSQL dataset, the model demonstrates considerable effectiveness in generating question-SQL pairs achieving accuracy of 80.63%, 98.937%, and 90% for exact match accuracy matrix, approximate string-matching, and manual evaluation, respectively. When testing the performance of the model on WikiSQL dataset, the model demonstrates efficiency in generating SQL queries, with an accuracy of 44.2% on WikiSQL and 94.26% for approximate string-matching. Results indicate improved performance with increased training epochs. This work highlights the potential of fine-tuned T5 model to convert medical-related questions written in natural language to Structured Query Language (SQL) in healthcare domain, providing a foundation for future research in this area.

Features of Quranic Text Decording as a Partly Dead Arabic Language

Purpose of the study. The purpose of the study is to systematize the meanings of words and cases of modification of grammatical categories of names and verbs based on the decipherment of the Koranic text. Materials and research methods. The sentences of the Koranic text were analyzed using a continuous sampling method within the framework of suras and verses. The research material is the text of the Koran. It should be noted that the earliest texts of the Qur'an in Arabic available in the world date back to 1400 years. The texts of letters with dots and vowels (haraka) date back 1200 years, they are fully consistent with the modern texts of the Qur'an distributed throughout the world in books and on the Internet (الكريم القرآن). The objects of the study were the meanings of words, changing the person of the verb, changing the tense or mood of the verb, changing the case of names, replacing one morphological structure with another, using a present tense verb in the imperative mood and vice versa, replacing an active participle with a passive participle and vice versa, modification of verb inflections. Based on scientific interest in the illogically composed ancient text, an attempt was made to systematize the meanings of words and cases of modification of grammatical categories throughout the text. Research results. It is noted that modern Arabic only partially corresponds to the language of the Quranic text of a thousand years ago. The reason for this is that the modern understanding of the meanings of many Quranic words is not systematized, that is, the same word in the text of the Quran can have several meanings, which are revealed not only from the context, but also from traditions about the messenger Muhammad (Hadis). It was revealed that in the works of researchers it is believed that the language of the text of the Quran is complex due to the fact that it was created on the basis of rhyme, incomprehensible logic of the text, or this text is encrypted. Because of this, it is generally accepted that the Quranic text is encrypted, the full decryption of which is known only to its author. The meanings of words and cases of modification of grammatical categories of names and verbs found in the text of the Koran are systematized. Conclusions. The text of the Koran is voluminous, which is why it is not possible to manipulate the meanings of words and inflections of the verbs of the text, since they are found in sentences with different meanings. It is concluded that contextual analysis is the most promising and optimal method for studying the text of the Koran, its translation, as well as systematization of linguistic phenomena, in particular, the meanings of words and modifications of grammatical categories.

Evaluasi Pembelajaran Membaca di Kelas Tinggi

Evaluation of reading learning in higher grades is a process to find out how many students are able to understand information, receive information, categorize, analyze and conclude information, accuracy of pronunciation and intonation when reading language texts. This research aims to determine the purpose of evaluating reading learning and the types of evaluation of reading learning in higher grades. The research method used is a qualitative research method with literature study. The research results show that the evaluation of reading learning aims to gain knowledge (information), foster the development of compassion and beauty, and fill free time. The types of evaluation of reading learning in higher grades are diagnostic evaluation, selective evaluation, formative evaluation, and summative evaluation.