Language Text Research Articles

Effectiveness of Using Google Translate in Second Language Learning: A Study Based on Hindi and Sinhala Languages

Google Translate and other machine translation programmes are starting to change how language is taught and learned. Language instructors and expert translators might have laughed at machine-generated writing a few years ago. At least some language pairings and texts contain both learner-created and machine-created languages due to the transition from statistical machine translation to more recent neural machine translation techniques that use artificial neural networks, and it can be challenging to discern between machine-created and learner-created languages. The present study aims to examine students’ perceptions of the effectiveness of using the Google Translate tool when translating academic documents. The participants of this study were 25 students who are currently studying for their Bachelor of Arts Honours Degree in Hindi as a second language at the University of Kelaniya, Sri Lanka. This study reveals that students tend to use Google Translate depending on the complexity of a certain document and the availability of relevant dictionaries. Furthermore, most students found it useful to use Google Translate, and they prefer partial translation. The majority of students agree that they post-edit the translation using Google Translate. This research also suggests strategies that can be adapted to use Google Translate effectively.

Research on Minority Character Recognition - Taking Small Seal Font as an Example

Text recognition is one of the important fields of computer vision and is widely used in automated office, assisted reading and other fields. With the continuous development of deep learning, the visual language text recognition method generated by the combination of optical character recognition and natural language processing involves the technology of recognizing text information from images or videos, which greatly improves the machine's understanding ability and interaction efficiency. However, for some niche fonts, such as Urdu, Xixia, and Xiaozhuan, these characters have complex structures, diverse strokes, and sloppy writing, making them a challenging field in text recognition. Taking Small Seal Scripts, also called Xiaozhuan, as an example, this paper summarizes the advantages and disadvantages of the current Xiaozhuan text recognition algorithm, and proposes algorithm improvement suggestions for the segmentation of Xiaozhuan characters on seals. Xiaozhuan fonts are quite different from modern Chinese characters, mainly reflected in the stroke structure, writing style, and the correlation between characters. It has smoother lines and many glyphs are unique. This complexity makes it difficult to directly apply traditional Optical Character Recognition (OCR) technology. Therefore, many studies have combined image preprocessing, character segmentation, and deep learning models for automatic recognition. Future research should focus more on the generalization and lightweight of the model so that it can run on devices with limited computing resources.

The Challenges of Fidelity in Translating English Proverbs into Arabic

Translation is the communication of the meaning of a source language text using an equivalent target-language text. The main problem of translation is meaning. Thus fidelity or faithfulness should be to meaning and not to the form, style, or even the context of the source language To achieve an accurate and appropriate translation the translator should also have a good background knowledge of the SL and the TL culture, s/he take into consideration the context of the text in the TL. The main problem here is that translators cannot achieve total fidelity in translating texts that belong to languages that have considerable gaps between their cultures, especially in translating proverbs. In every translation process, there is a purpose or a function that is not inherited in the words of the original text. One of the main challenges and problems in translating English proverbs into Arabic is the lack of linguistic equivalence in the Arabic language due to the differences between Arabic and English cultures, religion, social norms, etc. In this case, the translators may use direct literal translation which is inappropriate in translating proverbs because proverbs acquired their meaning from everyday experience which is inevitably different between Arabic and English. This study aims to explain the concept of fidelity in translation and how it may affect the translation process. Also, it aims to explore the challenges and difficulties of fidelity in translating English proverbs from English into Arabic. To fulfill the above aims, the current study hypothesizes that fidelity in translation is very important because it enables the translator to transpose the author's ideas and style accurately but sometimes this makes it difficult for the readers of the target language to understand the meaning effectively if the translator tries to achieve fidelity rather than the purpose of the source text. Also, translators who have a good background in both languages are more successful in their translation. Two proverbs as data for this study were chosen from the book entitled “One Thousand and One English Proverbs Translated into Arabic" written by Dr. Omer Osman Jabak, a lecturer at King Saud University, Saudi Arabia. Subsequently, these proverbs were given to ten M.A. students at the Department of Translation, the College of Arts, Tikrit University to render them into Arabic.

Alternate Paradigms of African Literary Discourse through Language

Language serves as a multifaceted tool in African literature, embodying cultural heritage, facilitating communication, and challenging colonial legacies. It often reflects the diversity of Africa’s linguistic landscape, incorporating indigenous languages alongside colonial tongues like English, French, and Portuguese. African writers employ language to preserve oral traditions, express cultural identities, and convey the complexities of post-colonial experiences. Moreover, language acts as a medium for resistance against hegemonic narratives, enabling writers to subvert stereotypes and reclaim narratives previously dominated by Western perspectives. Through linguistic innovation and experimentation, African literature continues to evolve, asserting its significance on the global literary stage while celebrating the richness of Africa’s linguistic heritage. This paper attempts to delineate the diverse paradigms that can emerge about the connotations of colonial African society, apartheid and its portrayal due to alternate discourses of language in texts. By examining the use of language as a tool in Chinua Achebe’s Arrow of God (1964) and Nadine Gordimer’s The Lying Days (1953), this paper delineates how language can portray the same story with different levels of consciousness.

How to present economic evaluations to non-technical audiences? Randomized trials with professionals and the general population

BackgroundCost-effectiveness analyses of health technologies have become a part of the decision-making process in healthcare policies. Nevertheless, economic results are not always presented in comprehensible formats for non-technical audiences, such as the general population, healthcare professionals or decision-makers. The purpose of this study was to observe which formats best convey the key message of an economic evaluation, and which are best received by two different audiences.MethodsThe summary of a hypothetical cost-effectiveness analysis was edited in different formats: infographic, plain language text and video-abstract for the general population; executive summary and policy brief for a specialized population, i.e. clinicians, clinical and non-clinical managers, or methodologists in health-technology assessment. Participants were randomly shown one of the formats, and data on objective and subjective comprehension, and perceived usefulness/acceptability were gathered by means of online questionnaires. Statistical differences between formats within each audience were analysed.ResultsIn the general population (N = 324), objective comprehension was statistically significantly better for infographic than for video-abstract (p = 0.005), and for plain text than for video-abstract (p = 0.024). There were no differences in subjective comprehension, but video-abstract was considered statistically significantly more useful to understand the information than plain text (p = 0.011). In the specialized population (N = 100), no statistically significant differences were observed for objective and subjective comprehension, although policy brief was perceived as statistically significantly more useful than executive summary (p = 0.005).ConclusionsA balance between effectivity of conveying the message and attractivity of the format needs to be sought, to facilitate non-technical audiences’ understanding of economic data and, consequently, perceive decision-making processes as more transparent and legitimate. The infographic and policy brief could be robust ways to present economic data to the general public and specialized audience, respectively.

Exploring the Development of Safety Culture among Physicians with Text Mining of Patient Safety Reports: A Retrospective Study.

Safety culture development is essential for patient safety in healthcare institution. Perceptions of patient safety and cultural changes are shown up in patient safety reports, however, were rarely investigated. The aim of this study was to investigate the perception of physicians and to explore the development of safety culture using quantitative content analysis for patient safety reports. A retrospective analysis of free descriptions of harmful patient safety reports submitted by physicians was performed. Natural language processing and text analysis were conducted using the 'KH Coder'. A co-occurrence analysis was performed in each period to identify and analyze the safety concepts. The study period was grouped into three for comparison. The patient safety reports from physicians were collected between April 2004 and March 2020. Of these, 3351 reports were harmful; 839 reports were included in period 1, 1016 reports in period 2, and 1496 reports in period 3. Natural language processing identified 316 307 words in the free descriptions of 3351 reports. We identified seven concepts from the cluster in co-occurrence analysis as follows: 'explanation of adverse event to patients and families,' 'central venous catheter,' 'intraoperative procedure and injury,' 'minimally invasive surgery,' 'life-threatening events,' 'blood loss,' and 'medical emergency team and critical care.' These seven concepts showed significant differences among the three periods, except for 'blood loss'. The 'explanation of adverse event to patients and families' decreased in proportion from 11.3% to 8.8% (p<0.05). The 'central venous catheter' decreased from 17.3% to 11.3% (p<0.01). Meanwhile, 'minimally invasive surgeries' and 'intraoperative procedures' increased from 3.9% to 12.9% (p<0.01) and from 10.8% to 14.6% (p<0.05), respectively. Focusing on patients' events, 'life-threatening events' decreased from 13.0% to 8.1% (p<0.01); however, 'medical emergency teams and critical care' increased from 3.3% to 10.6% (p<0.01). Free description in patient safety reports is useful for evaluating the safety culture. Co-occurrence analysis revealed multiple concepts of physicians' perceptions. Quantitative content analysis revealed changes in perceptions and attitudes, and a disclosure policy of adverse events and priority of patient care appeared with the development of safety culture.

Analysis of Datasets and Large Language Models for Vulnerability Detection in Imperative Programming Language Code

Large language models are machine learning models that enable the classification and generation of both natural language text and code in various programming languages. These models have billions of parameters and are trained on vast datasets. In recent years, such models have been successfully applied to a wide range of tasks in software engineering. The paper presents data on publication activity on the topic under study, which is obtained on the basis of statistical analysis of search results for relevant key queries. In addition, a review of recent publications in the field of using large language models to detect vulnerabilities in program code is carried out, and the results of the analysis of data sets that are used in training neural network models to search for vulnerabilities in program code are presented.

Redescription mining-based business process deviance analysis

AbstractBusiness processes often deviate from their expected or desired behavior. Such deviations can be either positive or negative, depending on whether or not they lead to better process performance. Deviance mining addresses the problem of identifying such deviations and explaining why a process deviates. In this paper, we propose a novel approach to identify and explain the causes of deviant process executions based on the technique of redescription mining, which extracts knowledge in the form of logical rules. By analyzing, comparing, and filtering these rules, the reasons for the deviant behaviors of a business process are identified both in general and for particular process instances. Afterward, the results of this analysis are transformed into a concise and well-readable natural language text that can be used by business analysts and process owners to optimize processes in a reasoned manner. We evaluate our approach from different angles using four process models and provide some advice for further optimization.

Dotless Arabic Text for Natural Language Processing

Abstract This article introduces a novel representation of Arabic text as an alternative approach for Arabic NLP, inspired by the dotless script of ancient Arabic. We explored this representation through extensive analysis on various text corpora, differing in size and domain, and tokenized using multiple tokenization techniques. Furthermore, we examined the information density of this representation and compared it with the standard dotted Arabic text using text entropy analysis. Utilizing parallel corpora, we also drew comparisons between Arabic and English text analysis to gain additional insights. Our investigation extended to various upstream and downstream NLP tasks, including language modeling, text classification, sequence labeling, and machine translation, examining the implications of both the representations. Specifically, we performed seven different downstream tasks using various tokenization schemes comparing the standard dotted text with dotless Arabic text representations. Performance using both the representations were comparable across different tokenizations. However, dotless representation achieves these results with significant reduction in vocabulary sizes, and in some scenarios showing reduction of up to 50%. Additionally, we present a system that restores dots to the dotless Arabic text. This system is useful for tasks that require Arabic texts as output.

Exploring the Impact of Different Textual Language Features on Large Language Models' Detection of Fake News

Abstract: With the proliferation of social media and online platforms, fake news has become increasingly rampant. This study explores the impact of different textual language features on large language models (such as ChatGPT) in detecting fake news. By extracting extreme emotional vocabulary and exaggerated syntactic words commonly found in fake news and calculating their TF-IDF values, this study analyzes their influence on large language models' ability to assess the veracity of news. The study found that the frequency of extreme emotional words is higher than that of exaggerated syntactic words and has a more significant impact on fake news detection by large language models. Furthermore, this study suggests that by carefully selecting and adjusting language features, the accuracy and stability of fake news detection can be improved, providing new insights for optimizing automated detection systems. These findings provide important references for improving the technology of automatic fake news detection, contributing to the construction of a safer and more reliable online environment.

Exploring servicescape in coastal and marine tourism: insights from text mining and application of Kano model

ABSTRACT Employing text mining and the Kano model, this study aimed to delineate and categorize the components of the coastal and marine tourism servicescape. The research utilized text mining technology to analyze 132,545 Korean language text entries from the Naver platform, identifying 80 key terms associated with the coastal and marine tourism servicescape. A network of these terms was then constructed, segmented into three clusters reflecting the servicescape dimensions: “ambient conditions”, “spatial layout and functionality”, and “signs, symbols, and artifacts”. These clusters were further distilled into 18 elements, establishing a comprehensive framework for analysis. Subsequently, the study utilized the Kano model to analyze 823 South Korean survey responses, categorizing the elements into four qualities: attractive, one-dimensional, must-be, and indifferent. The findings provided a nuanced understanding of the servicescape in coastal and marine tourism, offering theoretical contributions and practical implications for tourism management and policymaking.

The Design of a Script Identification Algorithm and Its Application in Constructing a Text Language Identification Dataset

Script identification is easier to implement than language identification, and its identification rate is very high. The fewer languages are identified when using a language identification algorithm, the higher the identification rate is. However, no systematic study on SI involving multiple languages and determining how to construct relevant language identification datasets has been conducted. Therefore, in this paper, we discuss and design a script identification algorithm and the construction of a language identification dataset based on script groups. The data sources in this paper comprise 261 different languages’ text corpora from the Leipzig Corpora Collection, which are grouped into 23 different script groups. In the Unicode encoding scheme, different scripts are arranged into different code regions. Based on this feature, we propose a written script identification algorithm based on regular expression matching, the micro F-score of which reaches 0.9929 in sentence-level script identification experiments. To reduce noise when constructing the language identification dataset for each script, a script identification algorithm is used to filter out other-script content in each text.

A Consecutive Interpreting Study of the Keynote Speech on the Opening Ceremony of the Conference on Dialogue of Asian Civilizations

As a real-time bilingual processing activity, consecutive interpreting consists of three stages:source language listening and identification, information memorization and target language production. This paper chooses the consecutive interpreting recording of keynote speech on the opening ceremony of the Conference on Dialogue of Asian Civilizations as the study text. The author manage to analysis the target language text from lexical level, syntactic level and textual level.

Deep Learning-Based Freight Recommendation System for Freight Brokerage Platform

Platform-based businesses in the logistics market are evolving under the influence of digital transformation. Transforming the freight market into an environment where various types of freight can be traded across multiple markets and locations. Freight brokerage platforms have revolutionized the trading relationship between freight owners and vehicle owners. However, this type of system has also introduced inefficiencies, such as unestablished contracts, leading to unnecessary costs and delays. To address this issue, a freight recommendation system can assist users in finding what they are looking for while aiming to reduce failed contracts. With current advances in deep learning, complex patterns based on users’ past behaviors and preferences can be learned, enabling more accurate and personalized recommendations. This study proposes a deep learning-based freight recommendation system to provide personalized services and reduce failed contracts on freight brokerage platforms. The system is built by creating a freight transaction dataset, classifying freight categories through natural language processing and text mining techniques, and incorporating externally derived data on transportation distances. The deep learning model is trained using Autoencoder, Word2Vec, and Graph Neural Networks (GNN), with recommendation logic implemented to suggest suitable freight matches for vehicle owners. This system is expected to increase the market efficiency of the freight logistics industry and is a key step toward improving the long-term profit structure.

Student machine translation use in a transnational English-medium instruction university

Abstract Research on machine translation in language courses has proliferated over the last decade, yet few studies have considered use in English medium-instruction programs that aim to simultaneously develop student language ability and knowledge of content. This study therefore investigates student use and beliefs about machine translation at a transnational Chinese university. Interviews with twenty-one undergraduate students and fourteen instructors found that student use of machine translation is widespread, though variable. Students report using machine translation to increase reading comprehension, vocabulary, and speed; translate first language writing to raise target language writing quality and complexity; and decrease the amount of copied (though cited) content. Instructors report student use of simultaneous speech translation in class, mistranslations in student writing indicating a lack of engagement with English language texts, and cross-language plagiarism. Both students and instructors view machine translation as a tool: a potentially beneficial one that, if used uncritically and constantly, can stunt language development. Students are thus faced with a choice between developing their English and the expediency of quickly reading and writing in Chinese, a choice heavily affected by student workload, performance pressures, and, arguably, lack of machine translation literacy.

Correlations and Fractality in Sentence-Level Sentiment Analysis Based on VADER for Literary Texts

We perform a sentence-level sentiment analysis study of different literary texts in English language. Each text is converted into a series in which the data points are the sentiment value of each sentence obtained using the sentiment analysis tool (VADER). By applying the Detrended Fluctuation Analysis (DFA) and the Higuchi Fractal Dimension (HFD) methods to these sentiment series, we find that they are monofractal with long-term correlations, which can be explained by the fact that the writing process has memory by construction, with a sentiment evolution that is self-similar. Furthermore, we discretize these series by applying a classification approach which transforms the series into a one on which each data point has only three possible values, corresponding to positive, neutral or negative sentiments. We map these three-states series to a Markov chain and investigate the transitions of sentiment from one sentence to the next, obtaining a state transition matrix for each book that provides information on the probability of transitioning between sentiments from one sentence to the next. This approach shows that there are biases towards increasing the probability of switching to neutral or positive sentences. The two approaches supplement each other, since the long-term correlation approach allows a global assessment of the sentiment of the book, while the state transition matrix approach provides local information about the sentiment evolution along the text.

The open corpus of the Vepsian and Karelian languages

In order to preserve and systematically study the Vepsian and Karelian languages, the staff of the Institute of Language, Literature and History and the Institute of Applied Mathematical Research of the KarSC RAS have created and continue to improve the VepKar language corpus. The project is aimed at preserving and accumulating written texts in Karelian and Vepsian languages, fixing and preserving oral speech, researching the Baltic-Finnish languages of Karelia, editing the norms of newly written versions of Karelian and Vepsian languages, and creating educational applications. Anyone can use VepKar as an electronic library and a full-fledged electronic dictionary, which makes this resource very popular.

Beyond Hormones: A Systematic Review of the Risk of Cardiovascular Diseases in Polycystic Ovary Syndrome.

Polycystic ovary syndrome (PCOS) is a common endocrinopathy among women in the reproductive age group. PCOS is defined by the Rotterdam criteria, which include hyperandrogenism, oligo-anovulation, and polycystic ovaries on ultrasound. The common symptoms are irregular or absent periods, acne, hirsutism, and alopecia androgenica. Increased prevalence of metabolic syndrome is seen among women with PCOS, which increases cardiovascular risk (CVR). Insulin resistance (IR), being most common in PCOS, is often seen in patients with obesity. IR and obesity induce chronic low-grade inflammation in PCOS, increasing various inflammatory markers. Increase in the parameters of tumor necrosis factor-alpha (TNF-alpha), interleukin-1 (IL-1), interleukin-6 (IL-6), C- reactive protein (CRP), plasminogen, endothelin-1, fibrinogen, cystatin-C, fetuin B, vascular endothelial growth factor, and endostatin levels have been documented in the PCOS-affected women. Microbiota alteration is also seen in this demographic, which increases metabolites like trimethylamine-N-oxide (TMAO). TMAO, because of its pro-atherosclerotic activity, is linked to an increase in CVR. In this systematic review, we intended to discover the causes and factors that lead to increased CVR in women diagnosed with PCOS. This systematic review used PubMed, regular keywords, and Google Scholar. The inclusion criteria included the human female population of all ages and literature available in the English language in free full text published between the years 2019 and 2024. The exclusion criteria included research involving animals, works published before 2019, articles written in a language other than English, and articles not publicly available. A total of 89 articles were identified, and 8 final articles were selected after quality assessment.

SEMANTICS AND MEANING OF TERMS WITH THE COMPONENT ’RED’

The article provides an analysis of the results of a study on the semantics and meanings of terms with the component red in modern Bulgarian. The language material on which basis the study was carried out is an electronic corpus of Bulgarian language texts compiled from publicly available internet sources. Statistical approaches to search for concordances and collocations available through the Sketch Engine lexicographic software were used to discover the terms in the corpus, and the order in the search procedures (algorithm) was previously tested in other cited studies. The theoretical approach of the study involves tracing the semantics and meanings of terms for the color red and terms with the component red using both traditional approaches such as the theory of nomination (by detection of the main semantic component), and modern theories, such as the theory of prototypes (by evaluation of color’s prototypes) in the analysis of the semantics of these terms. Results and examples of high-frequency contextual corpus usages of red are provided to analyze the terms’ semantics and meanings both as a color term and terms with other (non-color) meanings (prototypes and terms of the basic features of the prototypes), and related conclusions on semantics and meanings of these terms are provided. The color component of the terms at issue is interpreted in the context of a broader understanding of color (such as macro-color), in its transition to other colors, and it is also noted that those terms relate to similar terms with a color component, for example white, establishing a semantic relation through the macro-color component.

Classification of domestic violence Persian textual content in social media based on topic modeling and ensemble learning

ObjectiveDue to the importance of monitoring social networks to categorize domestic violence content and extract practical knowledge for conducting preventive interventions, as well as analyzing the extensive Persian textual content related to domestic violence generated in social networks following the COVID-19 pandemic, primarily, this research aims to create the best domestic violence Persian textual content classification model using topic modeling content at first and then combining algorithms using ensemble learning to achieve the best model performance. MethodBy collecting Persian textual data using hashtags related to domestic violence equally and randomly from Telegram, Twitter, and Instagram networks between April 2020 and April 2023, the content were considered for topic modeling using the LDA algorithm. By extracting the probabilities of each topic for each document in our dataset, we considered the topic that had the highest probability to be a label for that document. Following feature extraction from labeled datasets, the Stacking and Voting ensemble learning methods were applied. ResultThe analysis of 337,287 textual data revealed five topics: family crime news, war violence, women's rights, and violent reactions. Also, compared to the voting method, the stacking method performed better with 96.4577 precision, 96.4499 accuracy, 96.4499 recall, and 96.4475 F-score. ConclusionAccording to the study findings, practical knowledge of the extracted topics can assist mental health centers in making preventive decisions. Moreover, the built model has the most efficient performance among the built models for the multi-class classification of DV texts in the Persian language for social media monitoring.