Word Co-occurrence Research Articles

Methodological pluralism or multimethod research in health and medical sciences

<p><strong>Introduction.</strong> With the development of medical and health sciences, research problems have become more complex, and relying solely on quantitative or qualitative research approaches is no longer sufficient. The aim of this paper was to map the progress and trends in the scientific literature regarding the application of mixed studies in medical and health sciences.</p> <p><strong>Methods.</strong> Bibliometric analysis was applied to meet the established goals. The Dimensions database was selected for literature search. The analysis unit consisted of 485 publications of various types, and the VosViewer program was employed for the analysis.</p> <p><strong>Results. </strong>The results have shown that this topic in science has experienced an exponential decline in the last year. Additionally, the most productive authors, journals, and countries have been identified, along with their collaborative networks through visual co-citation analysis. Based on the analysis of word co-occurrence, we can conclude that words like “combined studies,” “problem,” “factors,” “community” are most frequently repeated, indicating that the topics addressed in this domain mainly focus on investigating and addressing numerous issues in the field of medical and health sciences.</p> <p><strong>Conclusion. </strong>Although the results indicate a decreased interest in this topic in the last year, it does not diminish the significance of this research. Progress in medical and health sciences requires a comprehensive approach to research, which includes both quantitative and qualitative methods, such as mixed studies. Understanding the current trends and available resources for researchers assists in achieving better outcomes and improving the quality of healthcare practice.</p>

Event Knowledge in Large Language Models: The Gap Between the Impossible and the Unlikely.

Word co-occurrence patterns in language corpora contain a surprising amount of conceptual knowledge. Large language models (LLMs), trained to predict words in context, leverage these patterns to achieve impressive performance on diverse semantic tasks requiring world knowledge. An important but understudied question about LLMs' semantic abilities is whether they acquire generalized knowledge of common events. Here, we test whether five pretrained LLMs (from 2018's BERT to 2023's MPT) assign a higher likelihood to plausible descriptions of agent-patient interactions than to minimally different implausible versions of the same event. Using three curated sets of minimal sentence pairs (total n = 1215), we found that pretrained LLMs possess substantial event knowledge, outperforming other distributional language models. In particular, they almost always assign a higher likelihood to possible versus impossible events (The teacher bought the laptop vs. The laptop bought the teacher). However, LLMs show less consistent preferences for likely versus unlikely events (The nanny tutored the boy vs. The boy tutored the nanny). In follow-up analyses, we show that (i) LLM scores are driven by both plausibility and surface-level sentence features, (ii) LLM scores generalize well across syntactic variants (active vs. passive constructions) but less well across semantic variants (synonymous sentences), (iii) some LLM errors mirror human judgment ambiguity, and (iv) sentence plausibility serves as an organizing dimension in internal LLM representations. Overall, our results show that important aspects of event knowledge naturally emerge from distributional linguistic patterns, but also highlight a gap between representations of possible/impossible and likely/unlikely events.

Identifying the driving factors of word co-occurrence: a perspective of semantic relations

Identifying the driving factors of word co-occurrence: a perspective of semantic relations

Exploring The Influence of Online Reviews on Sales

This study examined customer satisfaction in 20 lady-managed Indonesian restaurants located in South Korea, Japan, Taiwan, and Hong Kong, utilizing 1,316 online reviews. The overall average rating was 4.3 out of 5, reflecting high customer satisfaction. By using KH-coder software word frequency and co-occurrence network analysis identified key themes within reviews, encompassing food variety, pricing, restaurant ownership, and overall dining experience. The term "lady" was frequently associated with 5-star ratings, indicating a potential link between female management and high customer satisfaction. Conversely, terms like "expensive" and "small" were associated with lower ratings, highlighting the importance of perceived value for money and portion size. The study contributes to the underexplored area of female management in the restaurant industry. It demonstrates that women-led establishments can excel in competitive markets. It also underlines the importance of various aspects influencing customer satisfaction beyond just food, such as pricing and portion size. Furthermore, the findings validate the role of women as facilitators of cross-cultural culinary exchange and pave the way for future research on female leadership in the hospitality industry.

Scientific Knowledge Map Study of Therapeutic Landscapes and Community Open Spaces: Visual Analysis with CiteSpace

The pursuit of a healthy and comfortable living environment is a key developmental objective for human society. Therapeutic landscapes play a significant role in improving environmental conditions within these spaces. However, current research suggests that there is still much to be explored in this field, particularly in communal open spaces. Based on the Web of Science literature database and using the CiteSpace visualization tool, this study launched a literature review search on the existing research content on therapeutic landscapes in community spaces by means of publication volume analysis, spatial distribution, keyword analysis, co-citation clustering analysis, keyword timeline, and co-occurring word analysis. Based on the research results, the current status and direction of related research are outlined, and the research hotspots and future trends in this field are analyzed. Current research comprises multiple interdisciplinary branches, such as geography, public space, modern medicine, care, horticultural therapy, urban ecology, and more, with theoretical research, caregiving, spatial territories, and research methodology as the main research vectors. It is clear from this study that the current research on community therapeutic landscapes suffers from a lack of coordination between theoretical and practical development, and the related design practice activities are in a vulnerable stage of development. In terms of the population served, specialized research will be one of the directions of development, as there has been a gradual increase in the number of spatial research on the prevention and complementary treatment of various diseases for subdivided groups. At the same time, the research focus in this field has shifted from the physical health of users to their mental health, leading to a trend of public service development with the objective of social health.

A bibliometric analysis of the application of machine learning methods in the petroleum industry

With the emerge of Artificial Intelligence and Machin learning systems, the petroleum industry has witnessed a significant progress in its different disciplines to optimize decision making, time and costs. Despite the widespread application of using machine learning methods in the petroleum industry, a little attention has been devoted to build a framework to bring the main currents and researches on the topic. The current research is aimed at covering this gap through further analysis of complementary sources of bibliographic information, assessing 3163 bibliometric studies published in Web of Science (WOS) database. The descriptive statistics show that this field has an exponential growth in the last five years, such that more than 62 % of identified articles were published between 2018 and 2022. CHINA, IRAN and US are the pioneer countries with the highest number of publications on the application of artificial intelligence and machine learning in the upstream sector of the petroleum industry. The most influential journal in this field is ‘JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING’ (with 416 articles) (the current journal title is Geoenergy Science and Engineering) and the most productive author is SALAHELDIN ELKATATNY (with 54 articles) in WOS database. Also, the co-occurrence word analysis show that most of the artificial intelligence and machine learning applications in the upstream sector of the petroleum industry was the prediction and optimization in the field of ‘porosity’, ‘well logs’ and ‘permeability’. This paper contributes to the body of knowledge by providing a comprehensive overview of the application of artificial intelligence and machine learning in the upstream petroleum industry.

BibRank: Automatic Keyphrase Extraction Platform Using Metadata

Automatic Keyphrase Extraction involves identifying essential phrases in a document. These keyphrases are crucial in various tasks, such as document classification, clustering, recommendation, indexing, searching, summarization, and text simplification. This paper introduces a platform that integrates keyphrase datasets and facilitates the evaluation of keyphrase extraction algorithms. The platform includes BibRank, an automatic keyphrase extraction algorithm that leverages a rich dataset obtained by parsing bibliographic data in BibTeX format. BibRank combines innovative weighting techniques with positional, statistical, and word co-occurrence information to extract keyphrases from documents. The platform proves valuable for researchers and developers seeking to enhance their keyphrase extraction algorithms and advance the field of natural language processing.

Virtual education strategies in the context of sustainable health care and medical education: A topic modelling analysis of four decades of research.

The growing importance of sustainability has led to the current literature being saturated with studies on the necessity of, and suggested topics for, education for sustainable health care (ESH). Even so, ESH implementation has been hindered by educator unpreparedness and resource scarcity. A potential resolution lies in virtual education. However, research on the strategies needed for successfully implementing virtual education in the context of sustainable health care and medical education is sparse; this study aims to fill the gap. Topic modelling, a computational text-mining method for analysing recurring patterns of co-occurring word clusters to reveal key topics prevalent across the texts, was used to examine how sustainability was addressed in research in medicine, medical education, and virtual education. A total of 17 631 studies, retrieved from Web of Science, Scopus and PubMed, were analysed. Sustainability-related topics within health care, medical education and virtual education provided systematic implications for Sustainable Virtual Medical Education (SVME)-ESH via virtual platforms in a sustainable way. Analyses of keywords, phrases, topics and their associated networks indicate that SVME should address the three pillars of environmental, social and economic sustainability and medical practices to uphold them; employ different technologies and methods including simulations, virtual reality (VR), artificial intelligence (AI), cloud computing, distance learning; and implement strategies for collaborative development, persuasive diffusion and quality assurance. This research suggests that sustainable strategies in virtual education for ESH require a systems approach, encompassing components such as learning content and objectives, evaluation, targeted learners, media, methods and strategies. The advancement of SVME necessitates that medical educators and researchers play a central and bridging role, guiding both the fields of sustainable health care and medical education in the development and implementation of SVME. In this way, they can prepare future physicians to address sustainability issues that impact patient care.

Trend of research on the medical use of molecular hydrogen: a bibliometric analysis.

The medical use of molecular hydrogen, including hydrogen-rich water and hydrogen gas, has been extensively explored since 2007. This article aimed to demonstrate the trend in medical research on molecular hydrogen. A total of 1126 publications on hydrogen therapy were retrieved from the PubMed database until July 30, 2021. From 2007 to 2020, the number of publications in this field had been on an upward trend. Medical Gas Research, Scientific Report and Shock have contributed the largest number of publications on this topic. Researchers by the name of Xue-Jun Sun, Ke-Liang Xie and Yong-Hao Yu published the most studies in the field. Analysis of the co-occurrence of key words indicated that the key words "molecular hydrogen," "hydrogen-rich water," "oxidative stress," "hydrogen gas," and "inflammation" occurred most frequently in these articles. "Gut microbiota," "pyroptosis," and "COVID-19" occurred the most recently among the keywords. In summary, the therapeutic application of molecular hydrogen had attracted much attention in these years. The advance in this field could be caught up by subscribing to relevant journals or following experienced scholars. Oxidative stress and inflammation were the most important research directions currently, and gut microbiota, pyroptosis, and coronavirus disease 2019 might become hotspots in the future.

Individualisation, personalisation and person-centredness in mental healthcare: a scoping review of concepts and linguistic network visualisation

BackgroundTargeted mental health interventions are increasingly described as individualised, personalised or person-centred approaches. However, the definitions for these terms vary significantly. Their interchangeable use prevents operationalisations and measures.ObjectiveThis scoping review...

말뭉치 분석을 활용한 ‘붓다’, ‘쏟다’의 유의어 연구

Objectives This study aims to provide foundational insights for effective instruction and learning by examining the synonymous verbs ‘붓다 (to pour)’ and ‘쏟다 (to spill)’. The research focuses on analyzing the co-occurring words and semantic relationships within a corpus and visualizing the results through language networks.
 Methods Based on the examination of vocabulary usage in everyday conversation and spoken corpora of native Korean speakers, as well as the investigation of co-occuring words found in Korean language learner corpora, the semantic relationship and specific patterns of usage for ‘붓다’ (to pour) and ‘쏟다’ (to spill) were explored and visualized through language network analysis.
 Results The results of this study are as follows: while native Korean speakers use various extended meanings of ‘붓다’ (to pour) and ‘쏟다’ (to spill), Korean language learners tend to rely on a limited set of co-occuring words provided in Korean textbooks. It indicates an insufficient understanding of the expanded meanings of these synonyms among learners. Additionally, even intermediate and advanced Korean learners have been found to lack a comprehensive understanding of the co-occuring words of ‘붓다 (to pour)’ and ‘쏟다 (to spill)’, leading them to mistakenly interchange the two terms.
 Conclusions This study employs componential analysis to examine the distinctive characteristics of semantic relationships through the co-occurring words of ‘붓다 (to pour)’ and ‘쏟다 (to spill)’. Furthermore, we propose a suggested order of presenting meanings and representative co-occurring words for Korean language learners at different proficiency levels.

Human capital and business growth of the Startups: an approach to the state of the art

This document is a systematic and bibliometric review on the transitory characteristics and research trends in the last 7 years, on human capital and business growth in the field of startups. The exploration was carried out in the bibliographic database Scopus, obtaining 111 documents, including articles, book chapters, books, conferences and abstracts, in the areas of knowledge related to the topic. For this, the Biblioshiny web interface of the Bibliometrix package of the statistical program Rstudio was used, the file was processed in csv format downloaded from the Scopus metasearch engine. The results suggest a higher percentage of scientific articles (79 %), with a higher volume in 2021 (25 works), while the Journal of small business management stands out as having a great impact and one of the authors with the highest H index, was Frid C.J. with an indicator of 2 points, together with Gartner W.B., Nigam N. and Wyman D.M. Regarding the analysis of co-occurrences of words, the terms entrepreneuship and human capital are the most relevant and those that have the strongest relationship in the field of study.

Use Of the Quizizz Tool by Primary School Teachers: A Review of The Literature in Scopus from 2017-2023

The objective of this study was the analysis and quantification of the scientific literature by means of metrics in relation to the research consulted on the use of the Quizizz tool by primary education teachers during the period 2017-2023. Thus, the search was carried out through the combination of words "quizizz", "teachers". Likewise, the search filters were applied: Open access and the temporal criteria of 2017-2023, giving a total result of 45 studies that were exported in the Bibtex format for later reading with Cran-R. Among the results, it was identified that 2019 was the year with the highest scientific production with 10 studies. Regarding the productivity of authors, it was found that 2 authors have presented continuity with the topic studied. On the other hand, the relationship between the keywords of the author, the authors and the affiliations included was interpreted. The frequency of words showed that the keywords with the highest index are "motivation", "Online games", "quizizz", "Technology", "gamification", "assessment" and "online teaching - learning". The countries with the highest production are Indonesia and India. Word co-occurrence within the abstracts of all included studies was then analyzed with a sample of 100 words from all abstracts. The studies with the highest citation index present (n=130) and (n=90).

Use Of the Quizizz Tool by Primary School Teachers: A Review of The Literature in Scopus from 2017-2023

The objective of this study was the analysis and quantification of the scientific literature by means of metrics in relation to the research consulted on the use of the Quizizz tool by primary education teachers during the period 2017-2023. Thus, the search was carried out through the combination of words "quizizz", "teachers". Likewise, the search filters were applied: Open access and the temporal criteria of 2017-2023, giving a total result of 45 studies that were exported in the Bibtex format for later reading with Cran-R. Among the results, it was identified that 2019 was the year with the highest scientific production with 10 studies. Regarding the productivity of authors, it was found that 2 authors have presented continuity with the topic studied. On the other hand, the relationship between the keywords of the author, the authors and the affiliations included was interpreted. The frequency of words showed that the keywords with the highest index are "motivation", "Online games", "quizizz", "Technology", "gamification", "assessment" and "online teaching - learning". The countries with the highest production are Indonesia and India. Word co-occurrence within the abstracts of all included studies was then analyzed with a sample of 100 words from all abstracts. The studies with the highest citation index present (n=130) and (n=90).

Does ChatGPT have semantic understanding? A problem with the statistics-of-occurrence strategy

Over the last decade, AI models of language and word meaning have been dominated by what we might call a statistics-of-occurrence, strategy: these models are deep neural net structures that have been trained on a large amount of unlabeled text with the aim of producing a model that exploits statistical information about word and phrase co-occurrence in order to generate behavior that is similar to what a human might produce, or representations that can be probed to exhibit behavior similar to what a human might produce (meaning-semblant behavior). Examples of what we can call Statistics-of-Occurrence Models (SOMs) include: Word2Vec (CBOW and Skip-Gram), BERT, GPT-3, and, most recently, ChatGPT. Increasingly, there have been suggestions that such systems have semantic understanding, or at least a proto-version of it. This paper argues against such claims. I argue that a necessary condition for a system to possess semantic understanding is that it function in ways that are causally explainable by appeal to its semantic properties. I then argue that SOMs do not plausibly satisfy this Functioning Criterion. Rather, the best explanation of their meaning-semblant behavior is what I call the Statistical Hypothesis: SOMs do not themselves function to represent or produce meaningful text; they just reflect the semantic information that exists in the aggregate given strong correlations between word placement and meaningful use. I consider and rebut three main responses to the claim that SOMs fail to meet the Functioning Criterion. The result, I hope, is increased clarity about why and how one should make claims about AI systems having semantic understanding.

Measuring Cultural Diversity in Text with Word Counts

A long-standing concern in the study of culture is understanding how culture is distributed, often discussed in terms of cultural “coherence.” Cultural diversity, defined as the degree to which people share beliefs or meanings, is one dimension of cultural coherence that has been associated with many social outcomes. This article contributes to this area of research by considering how to measure cultural diversity in text and introducing a simple approach that uses word counts and sets of diversity indices (called “diversity profiles”). Text is useful for social-psychological analysis because as an artifact of individual thought, it provides a way to measure how beliefs and meanings are distributed and made salient across groups. The measurement approach outlined here contrasts to many contemporary computational approaches to measuring culture in text, which employ a relational logic of meaning based on word co-occurrences. While these more sophisticated approaches are well suited to measuring diversity in many instances, I show that there are some cases for which simpler measures based on word counts are ideal. After discussing the measurement of cultural diversity using word counts, I present a computational analysis of interview transcripts of American religious parents discussing the ages at which it is appropriate for children to participate in different practices often considered inappropriate for young children. The analysis points to the homogenizing influence of institutions on discussions of age appropriateness. I conclude by discussing implications for cultural analysis more generally.

Social Entrepreneurship: A Bibliometric Analysis of Its Fields of Study

Social Entrepreneurship, one of the branches of Entrepreneurship, has acquired important relevance because it has both social and financial purposes and has become a necessary mechanism to combat social exclusion, revitalize disadvantaged areas, provide employment opportunities, and address social problems neglected by the public and private sectors. In recent years, there has been a growing interest in the subject among academics and entrepreneurs, but there is still research that lacks an adequate conceptualization. Therefore, this article aims to identify the main lines of research on social entrepreneurship through a bibliometric analysis. To carry out the analysis, a study of both publication performance and word co-occurrence of 3023 articles extracted from Web of Science during the period 1993–2022 was performed using the SCIMAT v1.1.04 software for data processing. It was found that the year 2022 was the year with the highest scientific production, and a strong relationship was established between social entrepreneurship and gender and economic development issues. As a result of the analysis, possible lines of future research are proposed, and these can contribute to fill the existing knowledge gaps on social entrepreneurship and promote a better understanding of the topic.

MarQO: A query optimizer in multilingual environment for information retrieval in Marathi language

Information retrieval is a crucial component of modern information systems. A significant portion of the vast amount of information stored worldwide is in local languages. While most information retrieval systems are designed primarily for English, there is a growing need for these systems to work with data in languages other than English. Cross Language Information Retrieval (CLIR) systems play a pivotal role in enabling information retrieval across multiple languages. However, these systems often face challenges due to ambiguities in query translation, impacting retrieval accuracy. This paper introduces "MarQO," a query optimizer designed to address these challenges in the context of Marathi language. MarQO employs a multi-stage approach, including lexical processing, extraction of multi-word terms, synonym addition, phrasal translations, utilization of word co-occurrence statistics, and more. By disambiguating query keyword translations, MarQO significantly improves the accuracy of translations, thereby leading to more relevant document retrieval results.

Visualization and Analysis of the Data Mining Domain of Scientific Medical Publications in the Web of Science Database Using Co-word Analysis

Background: The present study aims at visualizing and analyzing the scientific productions of data mining in medical sciences in the Web of Science (WoS) database to identify knowledge structure in this field. Methods: The present study was carried out using co-word analysis, which is a scientometrics method. Also, the analysis of the WoS approach was used for the visualization and analysis of word co-occurrence networks in the data mining domain. The research population consisted of 14,430 records related to data mining published in WoS from 2008 to 2016. The data were analyzed after retrieval using VoSviewer scientometric software. Results: The research findings showed that the average annual growth rate of scientific productions in the data mining field had an upward trend and reached its peak in 2016. The findings also showed that the USA was the most productive country, with 8123 degrees, claiming the first rank by 27.4% of the total scientific production, and Iran was in the 13th rank with 678 degrees. Among universities and institutions, the Chinese Academy of Sciences (692 degrees), the University of California (621 degrees), and the National Center for Scientific Research (CNSR) (514 degrees) were ranked first to third globally. Among Iranian institutions and universities, Islamic Azad University (IAU), Tehran University, and Iran University of Technological Sciences were ranked 41st, 94th, and 200th in the world with 140, 105, and 66 degrees, respectively. The results of the current research also showed that the field of data mining was formed based on four vocabulary clusters with 108, 82, 73, and 42 words, respectively. Conclusions: The recognition of Iran as a successful country in the field of data mining should encourage researchers from other countries to communicate with Iranian scientists.

Analysing the longitudinal course selection panel data (2014-2020) of K-12 teachers from Zhejiang province: a comprehensive study on in-service training needs

ABSTRACT Effective teacher training programs rely on accurate need analysis. However, traditional data sources often limit the comprehensive understanding of teachers’ and stakeholders’ needs. This study examines panel data comprising K-12 teachers’ course selections from 2014 to 2020 on the management platform of Zhejiang Province, China. Employing quantitative analysis techniques such as word frequency, co-occurrence, variability, and regression analysis on the corpus of teacher training, this paper explores the significance of these analyses in understanding training needs. The results reveal significant patterns in word frequency, co-occurrence, difference, and regression analyses, aligning with previous studies. Based on these findings, we propose that training topics should address education reform, meeting the needs of government and school leaders. Additionally, training methods should integrate theory and practice to cater to the needs of training designers, and training programs should aim to enhance teachers’ teaching abilities. This paper contributes to the existing literature on training needs analysis, showcasing how the integration of teacher education and corpus linguistics expands cross-disciplinary research and addresses the issue of drawing unreliable conclusions from limited evidence. The research findings offer valuable insights to stakeholders.