Word Co-occurrence Research Articles

How user language affects conflict fatality estimates in ChatGPT

OpenAI’s ChatGPT language model has gained popularity as a powerful tool for problem-solving and information retrieval. However, concerns arise about the reproduction of biases present in the language-specific training data. In this study, we address this issue in the context of the Israeli–Palestinian and Turkish–Kurdish conflicts. Using GPT-3.5, we employed an automated query procedure to inquire about casualties in specific airstrikes, in both Hebrew and Arabic for the former conflict and Turkish and Kurdish for the latter. Our analysis reveals that GPT-3.5 provides 34 ± 11% lower fatality estimates when queried in the language of the attacker than in the language of the targeted group. Evasive answers denying the existence of such attacks further increase the discrepancy. A simplified analysis on the current GPT-4 model shows the same trends. To explain the origin of the bias, we conducted a systematic media content analysis of Arabic news sources. The media analysis suggests that the large-language model fails to link specific attacks to the corresponding fatality numbers reported in the Arabic news. Due to its reliance on co-occurring words, the large-language model may provide death tolls from different attacks with greater news impact or cumulative death counts that are prevalent in the training data. Given that large-language models may shape information dissemination in the future, the language bias identified in our study has the potential to amplify existing biases along linguistic dyads and contribute to information bubbles.

Matrix Factorization and Prediction for High-Dimensional Co-Occurrence Count Data via Shared Parameter Alternating Zero Inflated Gamma Model

High-dimensional sparse matrix data frequently arise in various applications. A notable example is the weighted word–word co-occurrence count data, which summarizes the weighted frequency of word pairs appearing within the same context window. This type of data typically contains highly skewed non-negative values with an abundance of zeros. Another example is the co-occurrence of item–item or user–item pairs in e-commerce, which also generates high-dimensional data. The objective is to utilize these data to predict the relevance between items or users. In this paper, we assume that items or users can be represented by unknown dense vectors. The model treats the co-occurrence counts as arising from zero-inflated Gamma random variables and employs cosine similarity between the unknown vectors to summarize item–item relevance. The unknown values are estimated using the shared parameter alternating zero-inflated Gamma regression models (SA-ZIG). Both canonical link and log link models are considered. Two parameter updating schemes are proposed, along with an algorithm to estimate the unknown parameters. Convergence analysis is presented analytically. Numerical studies demonstrate that the SA-ZIG using Fisher scoring without learning rate adjustment may fail to find the maximum likelihood estimate. However, the SA-ZIG with learning rate adjustment performs satisfactorily in our simulation studies.

Distributional hypothesis as isomorphism between word-word co-occurrence and analogical parallelograms.

Most of the modern natural language processing (NLP) techniques are based on the vector space models of language, in which each word is represented by a vector in a high dimensional space. One of the earliest successes was demonstrated by the four-term analogical reasoning task: what is to C as B is to A? The trained word vectors form "parallelograms" representing the quadruple of words in analogy. This discovery in NLP offers us insight into our understanding of human semantic representation of words via analogical reasoning. Despite successful applications of the large-scale language models, it has not been fully understood why such parallelograms emerge by learning through natural language data. As the vector space model is not optimized to form parallelograms, the key structure related to geometric shapes of word vectors is expected to be in the data, rather than the models. In the present article, we test our hypothesis that such parallelogram arrangement of word vectors readily exists in the co-occurrence statistics of language. Our approach focuses more on the data itself, and it is different from the existing theoretical approach trying to find the mechanism of parallelogram formation in the algorithms and/or vector arithmetic operations on word vectors. First, our analysis suggested that analogical reasoning is possible by decomposition of the bigram co-occurrence matrix. Second, we demonstrated the formation of a parallelepiped, a more structured geometric object than a parallelogram, by creating a small artificial corpus and its word vectors. With these results, we propose a refined form of distributional hypothesis pointing out an isomorphism between a sort of symmetry or exchangeability and word co-occurrence statistics.

Lexical Analysis of Japanese Children’s Songs from the Perspective of Japanese Language Education

Japanese children's songs can be a valuable resource for adult learners of the language, Firstly, the vocabulary used in children's songs is predominantly high-frequency and foundational, with a large portion falling below the N2 proficiency level. By mastering the words and phrases found in these songs, learners can expand their conversational abilities beyond the confines of traditional textbooks. Secondly, the songs offer a balanced representation of positive, negative, and neutral emotions, reflecting the emotional range and nuances of the Japanese language. This exposure to diverse emotional expressions can enrich learners' understanding of language and cultural nuances. Moreover, the study's semantic analysis reveals frequent word associations and co-occurrences, which can help learners grasp the interconnectedness of Japanese vocabulary and the cultural contexts in which words are used. Importantly, children's songs are deeply rooted in Japanese culture, indirectly exposing learners to various cultural aspects, such as the appreciation of nature, the concept of transience, and the importance of harmony and interconnectedness. This cultural immersion can foster a deeper understanding and appreciation of Japanese values and traditions. Furthermore, the familiar melodies and sing-along nature of these songs can facilitate social bonding and interpersonal connections among learners and native speakers. Engaging in shared cultural experiences, such as singing nursery rhymes together, can create a sense of community and strengthen the bond between learners and the target language culture.

Student Pharmacists’ Reflections on Personal and Professional Development Following a Series of Longitudinal Faith-Based Courses

ObjectiveThis study aims to explore how integrating Judeo-Christian religious education into the pharmacy curriculum influences students’ spiritual and moral development, professional values, and mindset, preparing them for future holistic health care practice. MethodsA series of Judeo-Christian faith-based courses were integrated into the required Doctor of Pharmacy curriculum with the aim of developing student pharmacists’ moral foundation for future health care roles. Student reflections from these courses were compiled and analyzed using WordStat, combining natural language processing and statistical analysis. Content analysis included word cloud visualization, word co-occurrence, phrase extraction, and topic modeling. Thematic analysis with a deductive coding approach was applied. Descriptive statistics were used to analyze coding frequencies. ResultsWord cluster analysis revealed frequently cited and co-occurring words, including God, patient, care, life, love, ethics, personal, professional, health, and people. The frequently occurring phrases included patient care, professional ethics, personal ethics, and professional developments. The topics with high coherence values included love and compassion, empathetic care, caring health professionals, patient care, and leadership based on biblical principles. The major themes that emerged included spiritual growth and connection with God, the cultivation of virtues and ethical principles in personal development, and integration of faith into professional commitments for these aspiring professionals. ConclusionThe findings suggest that integrating spiritual and religious education into the pharmacy curriculum helps students develop a strong moral foundation, guiding their future practice with compassion and integrity.

Dynamics of online debates: insights from textual network analysis

AbstractTextual data analysis is critical for monitoring changing themes over time. To overcome challenges posed by data richness, graph theory emerges as a tool for investigating word-topic associations. We present an approach to clustering co-occurrence word networks that prioritises network similarity quantification over time. Addressing theoretical and network geometrical constraints, a statistical framework for manifold data analysis facilitates the grouping of semantic networks, partitioning the observed time frame into periods, and identifying dominant topics in each period via tensor decomposition. The analysis of Brexit-related tweets demonstrates the efficacy of modern methods for identifying social media patterns on public discourse.

텍스트마이닝을 활용한 사회적 경제 분야 국내 연구 동향 분석 : 사회적기업과 소셜벤처를 중심으로

Social enterprises and social ventures, which constitute the actors of the social economy, are assuming the role of the 'third sector' by addressing a multitude of social issues that are not addressed by public and private companies. In this context, it is meaningful to gain an understanding of the characteristics of the social economy and the areas of study that fall within its remit. Accordingly, this study examines the defining characteristics and current research trends of social enterprises and social ventures, two pivotal entities within the social economy. Furthermore, it employs big data methods to analyse extensive data sets pertaining to social enterprises and social ventures. The abstracts of academic research on social enterprises and social ventures were extracted from the Research Information Service (RISS). Subsequently, three analytical techniques were employed in sequence: 1) word frequency and TF-IDF analysis, 2) co-occurrence network analysis, and 3) LDA-based topic modelling analysis. The results of the word frequency and TF-IDF analysis indicate that the economic aspects of social enterprises and their analysis represent a significant research area. Additionally, it can be concluded that research on organisational characteristics, their impact, and the value of the social economy is actively being conducted. Secondly, the 'co-occurrence word network' analysis demonstrated that research pertaining to social enterprises constitutes a pivotal topic within this field. The data analysis outcomes are of considerable significance, with the economic aspect of social enterprises and the purpose of enterprises also representing major research topics. Thirdly, eight themes were extracted through a comprehensive consideration of the implications of LDA-based topic extraction. The results demonstrated that social enterprises and social ventures are active in a multitude of fields, with a particular focus on the role and importance of various aspects, including consumer relations, support for individuals with disabilities, cultural access, urban regeneration, investment and responsibility, and partnerships. This demonstrates that social economy enterprises are playing a role in addressing social issues. The value of this study lies in its ability to draw inferences from the aforementioned research trends in the social economy domain through the utilisation of text mining analytical techniques. This study aspires to contribute to the formulation of prospective research directions in the field of social economy, with a foundation in the analysed research trends.

What kind of education research was funded by the government in China? A decade-long investigation (2010–2020)

PurposeThe key purpose of this study is to systematically examine the landscape of education research funded by the National Plan of Educational Research Funding (NPERF) in China. The study aims to: (1) identify the thematic focus areas that reflect the national education agenda, (2) analyze the general funding patterns of education research projects and (3) gain insights into the distinctive nature of the research agenda in China. The study employs a rigorous data-driven approach to offer valuable insights into the dynamic discourses within the field of education research in China, which has received relatively little attention despite its potential significance.Design/methodology/approachIn this study, we utilized word co-occurrence analysis and corpus-based frequency analysis to analyze the research projects funded by the National Plan of Educational Research Funding (NPERF) from 2011 to 2020.FindingsThe key characteristics of these projects highlight the focus on higher education research, addressing the interests of specific cohorts of students, teachers and disadvantaged populations. Furthermore, these projects demonstrate a remarkable responsiveness to the policy needs of the country and a robust inclination toward an international comparative framework.Research limitations/implicationsThe findings offer valuable insights into the landscape and features of funded education research in China, revealing a strong emphasis on addressing practical needs and enhancing the capacity of the education system in the country.Originality/valueThis paper presents a systematic examination of the topics covered in funded research under the National Plan of Educational Research Funding (NPERF) scheme from 2011 to 2020. It contributes to the advancement of understanding regarding knowledge traditions and practices in the Chinese context. Methodologically, this paper is the first in the literature to be prototyped with a word co-occurrence analysis approach to systematically investigate the funded education research in China. Additionally, it includes the development of a comprehensive corpus list to uncover the key characteristics of the funded projects. The analysis provides unique insights into the priorities and directions of education research supported by the Chinese government, which are of potential interest to international readers.

Mapping the Future of Innovation: A Bibliometric Analysis of STEM Education Trends in K-12

With the increasing global emphasis on science, technology, engineering, and mathematics (STEM) education, it is crucial to systematically assess research trends and thematic developments in this field. This paper utilizes bibliometric analysis to examine the scholarly landscape of K-12 STEM education, analyzing 2,645 articles from the Web of Science Core Collection between 2019 and 2023. By analyzing citation metrics, author productivity, institutional and country contributions, and exploring word co-occurrence networks, the study identifies predominant themes and patterns in the field. The findings reveal that advanced countries dominate STEM education research, reflecting significant scholarly interest and investment. The analysis also highlights the role of technological advancements and educational reforms in shaping research trajectories. Additionally, a robust pattern of international collaboration is uncovered, where strategic partnerships across diverse institutions utilize multidisciplinary approaches to effectively address educational challenges. The study suggests the need to broaden research databases to include non-English sources and extend analysis over longer periods for a better understanding of evolving trends. Furthermore, the study recommends embracing interdisciplinary methods and integrating emerging technologies to enhance teaching practices.

Developmental changes in the word co-occurrences of Spanish–English bilingual children with and without developmental language disorder

Purpose The study aimed to understand how bilingual children with typical language development (TLD) and those with developmental language disorder (DLD) use frequent word co-occurrences in their narratives. Method We studied the change over time in the word co-occurrences used by 30 Spanish-English bilingual children with and without DLD (experimental group). An additional normative group consisted of 98 TLD Spanish-English bilingual first graders. Children narrated two Spanish and two English stories in kindergarten and first grade. Employing a Python program on the transcribed narratives, we extracted all adjacent two-word and three-word co-occurrences. From the normative group, the 90 most frequently occurring two-word and 90 most frequently occurring three-word co-occurrences were extracted. The type and tokens of word co-occurrences each child in the experimental group produced out of the 180 identified word co-occurrences were analysed. Result Overall, children at first grade produced more word co-occurrences types than in kindergarten. Children with DLD used fewer types of word co-occurrences but produced them as often as than their TLD peers. Children with DLD increased their word co-occurrences from kindergarten to first grade at the same rate although at a lower frequency. Children in both groups produced similar types and tokens of word co-occurrences in both Spanish and English, except tokens of two word co-occurrences. Children produced two word co-occurrences more often in English than in their Spanish narratives. Conclusion The results shed light on children with DLD’s deficits in production of word co-occurrences, indirectly reflecting possible deficits in statistical pattern detection.

INOVAÇÃO E TEORIA ATOR-REDE

Sociology of innovation is a transdisciplinary approach to research and development whose theoretical-methodological framework is based on the Actor-Network Theory. The article aims to map scientific communications produced in the literature about innovation and Actor-Network Theory (ANT). As bibliographical research, a semiotic-material approach is used to map these research interests on the Dimensions scientific data platform, seeking to explore their problematization networks via word co-occurrence and clustering with the VOSviewer network analysis software, in addition to describing their translational movements based on the titles and summaries of the mapped research itself and the literature itself in some way related. Two problematization networks about innovation and ANT are explored, one being multidisciplinary (see relationships between different fields of research such as health, management and technologies) and the other interthematic (through actor-words such as “implementation”, “framework” and “knowledge”. Such research interests are crossed by the very semiotic-material complexity of scientific production and communication related to ANT and innovation, performing socio-technical and political-economic processes of invention, negotiation and commercialization between human and non-human actors, of somehow linked to different knowledge and domains of scientific and technological knowledge.

Exploring the grief journey of bereaved spouses: Insights into the impact of sedation within palliative care

ABSTRACT Despite being the most susceptible to psychopathological consequences, bereaved cancer spouses remain a vastly understudied group. Addressing this gap, a dedicated French research team comprising clinical psychologists, philosophers, and physicians is conducting an ongoing study focused on the emotional and affective complexities experienced by bereaved spouses after the loss of a loved one due to oncological diseases within the realm of palliative care and Continuous Deep Sedation until Death (CDSD). The preliminary data presented below concern the analysis of nine semi-directed interviews analyzed using ALCESTE software, a tool for statistical analysis of textual data based on word co-occurrences. Analysis and comparison of the thematic categories that emerged showed that social support, deep loneliness, and the preservation of memories emerged as central elements of the marital bereavement experience. In addition, the study highlights the critical role of comprehensive palliative care in France and the delicate nature of sedation experiences, revealing intense emotions such as helplessness and anger. Clear communication and active family participation in end-of-life decisions emerged as crucial factors in alleviating the grieving process in spouses. These findings provide valuable insights for healthcare professionals and counselors and underscore the critical need for further research focused on sedation.

Wealth/Poverty Opposition in English and Kazakh: A Comparative Study

This research employs linguistic concept modeling to scrutinize the semantic variations between notions of wealth and poverty in English and Kazakh, aiming to discern their contrasting characteristics. The corpora in English and Kazakh encompassing varied genres, styles, and thematic content were examined through word frequency and co-occurrence to elucidate divergent contextual frames for the terms in both languages, unveiling cultural nuances and cognitive associations. The results indicate that in English, wealth is majorly associated with financial prosperity, real estate ownership, and the availability of resources for a comfortable lifestyle. Conversely, in Kazakh-language writings, the concept of wealth extends to encompass elements like family connections and devotion to the homeland, reflecting cultural priorities and lifestyles. A comparable distinction is evident when examining the contextual usage of the term poverty. The findings underscore the significance of exploring linguistic concepts within distinct cultural and cognitive frameworks, opening avenues for further comparative linguistic research.

Global Highly Cited Publication Trends and Research Hotspots in Osteoporosis and Bone Metabolic Cells: A Bibliometric and Visualization Analysis from 2013 to 2023.

Bone metabolic diseases such as osteoporosis are caused by disruption of the metabolic balance between osteoblasts and osteoclasts. Thousands of papers have been published on osteoporosis and bone metabolizing cells. The purpose of this study is to draw the publication trend of highly cited literature in this field through bibliometrics and to explore the research hotspot analysis. This paper provides a comprehensive analysis of the impact of countries/regions, research institutions, authors, keywords, relevant journals, and references in the field of osteoporosis and bone metabolic cells research, with a specific focus on the theme of "Osteoporosis and bone metabolic cells". Furthermore, utilizing bibliometric methods, the study aims to offer valuable insights and references for future research endeavors, as well as clinical prevention and treatment strategies in this domain. The Web of Science [WOS] Core Collection database was examined in order to identify articles with high citation counts from 2013 to 31 October 2023. The citation counts, authors, year of publication, source, journal, geographical origin, subject, article type, and level of evidence were further analyzed using the R bibliometric package. The VOSviewer software was utilized to visualize word co-occurrence in a total of 251 articles. Our search strategy included 251 highly cited articles published between 2013 and 2023 in the field of osteoporosis and bone metabolic cells. The number of publications in this field remains consistently high, indicating ongoing research interest. Notably, the United States has made significant achievements and contributions in this area. Xie Hui, Cao Xu, and Goodman, Stewart are among the main contributors to these advancements. NATURE MEDICINE has the highest journal impact factor of 82.9, highlighting its prominence. The JOURNAL OF BONE AND MINERAL RESEARCH ranks first with 1,322 citations. Keyword research topics in this field include osteoclast differentiation, osteoblast differentiation, and mesenchymal stem cells. Through citation analysis, we found that 195 articles have been cited more than 100 times, demonstrating their significance and impact. This study analyzed the relationship between osteoporosis and bone metabolic cells using a bibliometric method. The results of these analyses can help researchers gain a more direct and scientific understanding of trends in the field. Additionally, it can provide guidance in identifying hot research directions and offer new ideas for the prevention and treatment of osteoporosis.

Natural Language Processing for Ancient Greek

Abstract Computational methods have produced meaningful and usable results to study word semantics, including semantic change. These methods, belonging to the field of Natural Language Processing, have recently been applied to ancient languages; in particular, language modelling has been applied to Ancient Greek, the language on which we focus. In this contribution we explain how vector representations can be computed from word co-occurrences in a corpus and can be used to locate words in a semantic space, and what kind of semantic information can be extracted from language models. We compare three different kinds of language models that can be used to study Ancient Greek semantics: a count-based model, a word embedding model and a syntactic embedding model; and we show examples of how the quality of their representations can be assessed. We highlight the advantages and potential of these methods, especially for the study of semantic change, together with their limitations.

Graph Contrastive Topic Model

Contrastive learning has recently been introduced into neural topic models (NTMs) to improve latent semantic discovery, but existing methods suffer from the sample bias problem owing to word frequency-based sampling strategy, which may result in false negative samples with similar semantics to the prototypes. We propose the novel graph contrastive neural topic model (GCTM), based on the graph-based sampling strategy, guided by the in-depth correlation and irrelevance information among documents and words. We model the input document as the document word bipartite graph (DWBG) and construct positive and negative word co-occurrence graphs (WCGs), to capture in-depth semantic correlation and irrelevance among words. Based on the DWBG and WCGs, we design the document-word information propagation (DWIP) process to perform the edge perturbation of DWBG, based on multi-hop correlations/irrelevance among documents and words. This yields the desired negative and positive samples, which are utilized for GCL together with the prototypes to improve learning document topic representations and latent topics. Experiments on several benchmark datasets demonstrate the effectiveness of our method for topic coherence and document representation learning compared with existing state-of-the-art methods.

The primacy of experience in language processing: Semantic priming is driven primarily by experiential similarity

The organization of semantic memory, including memory for word meanings, has long been a central question in cognitive science. Although there is general agreement that word meaning representations must make contact with sensory-motor and affective experiences in a non-arbitrary fashion, the nature of this relationship remains controversial. One prominent view proposes that word meanings are represented directly in terms of their experiential content (i.e., sensory-motor and affective representations). Opponents of this view argue that the representation of word meanings reflects primarily taxonomic structure, that is, their relationships to natural categories. In addition, the recent success of language models based on word co-occurrence (i.e., distributional) information in emulating human linguistic behavior has led to proposals that this kind of information may play an important role in the representation of lexical concepts. We used a semantic priming paradigm designed for representational similarity analysis (RSA) to quantitatively assess how well each of these theories explains the representational similarity pattern for a large set of words. Crucially, we used partial correlation RSA to account for intercorrelations between model predictions, which allowed us to assess, for the first time, the unique effect of each model. Semantic priming was driven primarily by experiential similarity between prime and target, with no evidence of an independent effect of distributional or taxonomic similarity. Furthermore, only the experiential models accounted for unique variance in priming after partialling out explicit similarity ratings. These results support experiential accounts of semantic representation and indicate that, despite their good performance at some linguistic tasks, the distributional models evaluated here do not encode the same kind of information used by the human semantic system.

Navigating the Evolution of Game-Based Educational Approaches in Secondary STEM Education: A Decade of Innovations and Challenges

The need to support science, technology, engineering, and mathematics (STEM) learning in secondary education is reflected in the ongoing investigation of innovative pedagogical practices, including game-based learning (GBL). Using an analysis of scholarly publications based on word co-occurrence, this study aimed to identify the main research themes addressed in the past decade by the scholarly community on game-based teaching and learning solutions in the context of STEM education in secondary schools, their evolution over time, and the key issues addressed in recent years. After a systematic selection, the titles and abstracts of the publications were collected in a text corpus and analyzed using T-LAB software version 7.2.1.4 (2022). A preliminary visual exploration of the keywords was performed to obtain an overall view of the issues addressed by the research. Specificity analysis was then applied to identify, for each subset of the corpus identified by the years of publication, the evolution of themes reflected in a change in the frequency of lemma use. Finally, to explore the most recent topics, the main thematic clusters of publications in the last three years were identified (thematic analysis of elementary contexts). The results suggest some changes in the issues addressed over the past decade, such as a shift in focus from the specific technologies and competitive elements of games to understanding how GBL can support engagement, motivation, and understanding of complex scientific concepts. The five key thematic clusters identified (“Experience”, “Application”, “Validation”, “Emotion”, and “Programming”) also indicate a stronger emphasis by the latest publications on the experiential and emotional components of learning, the need for empirical studies, and the integration of computational thinking and coding into GBL. Overall, this study indicates that GBL has the potential to become an integrated component of STEM education, evolving with pedagogical and technological innovations.

Satisfaction of customers in Airbnb: a cross-culture text mining of online reviews

ABSTRACT Diverse cultural values significantly influence consumer preferences and need for accommodations. Understanding these differences is crucial for managing satisfaction in peer-to-peer (P2P) settings. This study examines the differences and similarities in experience attributes reflected in consumer reviews across various languages. Employing Word Co-occurrence Networks, Factor Analysis, and Linear Regression, this study scrutinizes 243,215 Airbnb review texts in Chinese, Japanese, Korean, and English. Findings emphasize the significance of specific attributes in reviews across these languages, including convenience, location, Indoor facilities and service quality. However, the attributes affecting satisfaction show significant variations based on cultural and linguistic contexts, emphasizing the necessity for culturally sensitive business strategies.

Experimental study on short-text clustering using transformer-based semantic similarity measure

Sentence clustering plays a central role in various text-processing activities and has received extensive attention for measuring semantic similarity between compared sentences. However, relatively little focus has been placed on evaluating clustering performance using available similarity measures that adopt low-dimensional continuous representations. Such representations are crucial in domains like sentence clustering, where traditional word co-occurrence representations often achieve poor results when clustering semantically similar sentences that share no common words. This article presents a new implementation that incorporates a sentence similarity measure based on the notion of embedding representation for evaluating the performance of three types of text clustering methods: partitional clustering, hierarchical clustering, and fuzzy clustering, on standard textual datasets. This measure derives its semantic information from pre-training models designed to simulate human knowledge about words in natural language. The article also compares the performance of the used similarity measure by training it on two state-of-the-art pre-training models to investigate which yields better results. We argue that the superior performance of the selected clustering methods stems from their more effective use of the semantic information offered by this embedding-based similarity measure. Furthermore, we use hierarchical clustering, the best-performing method, for a text summarization task and report the results. The implementation in this article demonstrates that incorporating the sentence embedding measure leads to significantly improved performance in both text clustering and text summarization tasks.