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Journal articles on the topic 'Co-occurrence word analysis'
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Jin, Chun Xia, Hui Zhang, and Qiu Chan Bai. "Text Clustering Algorithm of Co-Occurrence Word Based on Association-Rule Mining." Applied Mechanics and Materials 599-601 (August 2014): 1749–52. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1749.
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According to the analysis of text feature, the document with co-occurrence words expresses very stronger and more accurately topic information. So this paper puts forward a text clustering algorithm of word co-occurrence based on association-rule mining. The method uses the association-rule mining to extract those word co-occurrences of expressing the topic information in the document. According to the co-occurrence words to build the modeling and co-occurrence word similarity measure, then this paper uses the hierarchical clustering algorithm based on word co-occurrence to realize text clustering. Experimental results show the method proposed in this paper improves the efficiency and accuracy of text clustering compared with other algorithms.
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Gallagher, Gillian. "Perceptual distinctness and long-distance laryngeal restrictions." Phonology 27, no. 3 (2010): 435–80. http://dx.doi.org/10.1017/s0952675710000217.
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In this paper, I present an analysis of the typology of laryngeal co-occurrence restrictions based on contrast markedness. The key ingredient of the analysis, for which I provide experimental support, is that laryngeal co-occurrence phenomena reflect a preference for maximising the perceptual distinctness of contrasts between words (Flemming 1995, 2004). An AX discrimination task finds that the contrast between an ejective and a plain stop is less accurately perceived in the context of another ejective in the word than in the context of another plain stop in the word. Pairs of words like [k'ap'i] and [k'api], which contrast 2vs. 1 ejectives, are less reliably distinguished than pairs of words like [kap'i] and [kapi], which contrast 1vs. 0 ejectives. The unifying factor of all laryngeal co-occurrence patterns is the neutralisation of the contrast between words with one and two laryngeally marked segments, exactly the contrast that is shown to be relatively perceptually weak.
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Rahman, Parinda, and Ifeoma Adaji. "Health Misinformation Vs. Facts on Social Media: Co-Occurrence Network Analysis in Bangladesh." European Conference on Social Media 11, no. 1 (2024): 359–67. http://dx.doi.org/10.34190/ecsm.11.1.2336.
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The increased usage of social media provides a way to disseminate health-related information more quickly. Alternatively, sharing health content on social media poses risks due to unrestricted posting, enabling misinformation to spread. Regional social and cultural contexts influence themes in social media posts, underscoring the importance of understanding content and prevalent misinformation themes. This insight is crucial for tailoring interventions, resource allocation, misinformation detection algorithms, and policy formulation. We conducted word co-occurrence network analysis, creating and analyzing two networks for valid information and misinformation in Bangladesh. The prevalence of misinformation regarding natural ingredients and treatments in Bangladesh underscores the need for targeted efforts to combat health misinformation on social media. For each network, we computed metrics such as betweenness, Katz centrality, out-degree, and degree distribution. Furthermore, we computed the Louvain clustering algorithm to identify word clusters. A comparative analysis of both networks suggested that the context of words used in sentences was important and that both networks contained information about natural remedies or ingredients for health benefits. The misinformation network contained the word raw turmeric with the highest bigram frequency of 162. These natural remedies were stated as cures, and there was much misinformation and valid information surrounding common health conditions such as blood pressure. This was depicted through the word blood having an outdegree of four and seven in the misinformation and valid information networks, respectively. The valid information network emphasized the beneficial properties of natural ingredients rather than their supposed ability to cure diseases. This study provides insights into the distinctions and parallels between valid health information and misinformation on social media, considering their social and cultural context. It underscores shared semantics and bigram words between them, suggesting that understanding these differences can aid in addressing region-specific challenges.
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Torii, Takuma, Akihiro Maeda, and Shohei Hidaka. "Distributional hypothesis as isomorphism between word-word co-occurrence and analogical parallelograms." PLOS ONE 19, no. 10 (2024): e0312151. http://dx.doi.org/10.1371/journal.pone.0312151.
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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.
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Zhang, Wei, Zhonglin Ye, Haixing Zhao, Jingjing Lin, and Xiaojuan Ma. "TAMNR: a network embedding learning algorithm using text attention mechanism." PeerJ Computer Science 9 (December 11, 2023): e1736. http://dx.doi.org/10.7717/peerj-cs.1736.
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Because many existing algorithms are mainly trained based on the structural features of the networks, the results are more inclined to the structural commonality of the networks. These algorithms ignore the rich external information and node attributes (such as node text content, community and labels, etc.) that have important implications for network data analysis tasks. Existing network embedding algorithms considering text features usually regard the co-occurrence words in the node’s text, or use an induced matrix completion algorithm to factorize the text feature matrix or the network structure feature matrix. Although this kind of algorithm can greatly improve the network embedding performance, they ignore the contribution rate of different co-occurrence words in the node’s text. This article proposes a network embedding learning algorithm combining network structure and co-occurrence word features, also incorporating an attention mechanism to model the weight information of the co-occurrence words in the model. This mechanism filters out unimportant words and focuses on important words for learning and training tasks, fully considering the impact of the different co-occurrence words to the model. The proposed network representation algorithm is tested on three open datasets, and the experimental results demonstrate its strong advantages in node classification, visualization analysis, and case analysis tasks.
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Jiang, Zhongqiang, Dongmei Zhao, Jiangbin Zheng, and Yidong Chen. "A Study on Differences between Simplified and Traditional Chinese Based on Complex Network Analysis of the Word Co-Occurrence Networks." Computational Intelligence and Neuroscience 2020 (December 3, 2020): 1–8. http://dx.doi.org/10.1155/2020/8863847.
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Currently, most work on comparing differences between simplified and traditional Chinese only focuses on the character or lexical level, without taking the global differences into consideration. In order to solve this problem, this paper proposes to use complex network analysis of word co-occurrence networks, which have been successfully applied to the language analysis research and can tackle global characters and explore the differences between simplified and traditional Chinese. Specially, we first constructed a word co-occurrence network for simplified and traditional Chinese using selected news corpora. Then, the complex network analysis methods were performed, including network statistics analysis, kernel lexicon comparison, and motif analysis, to gain a global understanding of these networks. After that, the networks were compared based on the properties obtained. Through comparison, we can obtain three interesting results: first, the co-occurrence networks of simplified Chinese and traditional Chinese are both small-world and scale-free networks. However, given the same corpus size, the co-occurrence networks of traditional Chinese tend to have more nodes, which may be due to a large number of one-to-many character/word mappings from simplified Chinese to traditional Chinese; second, since traditional Chinese retains more ancient Chinese words and uses fewer weak verbs, the traditional Chinese kernel lexicons have more entries than the simplified Chinese kernel lexicons; third, motif analysis shows that there is no difference between the simplified Chinese network and the corresponding traditional Chinese network, which means that simplified and traditional Chinese are semantically consistent.
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Kobayashi, Yuka, Daisuke Yamamoto, and Miwako Doi. "Word Co-occurrence Analysis with Utterance Pairs for Spoken Dialogue System." Transactions of the Japanese Society for Artificial Intelligence 28, no. 2 (2013): 141–48. http://dx.doi.org/10.1527/tjsai.28.141.
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Sedighi, Mehri. "Using of co-word analysis method in mapping of the structure of scientific fields(case study: The field of Informetrics)." Iranian Journal of Information Processing & Management 30, no. 2 (2015): 373–96. https://doi.org/10.5281/zenodo.14036052.
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Based on the co-occurrence analysis method, the scientific fields can be extracted and the relationships between them can be detected directly from their subject content. With this view, the aim of this study is to determine of the subfields of informetrics and the relationships between of these subfields, and thus to investigate the efficiency of this method and its application in mapping of the structure of scientific fields.This is an applied study using scientometrics, co-word analysis and network analysis methods. in order to determine the main concepts in the field of informetrics, all the scientific papers of international scholars in this field were extracted from WOS (included ٧٣٧٥ record from 1991 to 2012). Then after refining and standardizing of the keywords of these articles, a selected list of these keywords was prepared. Using the results of the data analysis, the trend in publication growth in the field of informetrics and each of its subfields was identified.Then the thematic maps were drawn using Vosviewer and Nodexl software. The resulting data of the maps and the structure of the formed clusters and their inter-relationships have been analysed. Based on the resulted maps the concepts such as information science, library, bibliometric analysis, innovation and text mining are the most widely used topics in the field of informetrics. The co-word occurrence maps drawn at the different time periods show the changes and stabilities in the concepts and terms related to the field of informetrics. Some words like "bibliometric analysis" are present in all the studied years, while others disappear over time. In interacting with developments and new technologies, the new concepts can be created.
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Deepa Azhchath Vasu, Amritha Achuthkumar, Revathy Arya Suresh. S, and Tony Grace. "Scientometric analysis of mammalian microbiome research." JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH 2, no. 7 (2021): 1–14. http://dx.doi.org/10.46947/joaasr272020111.
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Over the past few decades, microbiota research has been gaining the attention of the researchers working on the concepts of health–enhancement and overall–wellbeing. The mammalian microbiome has been progressively acknowledged as a developing research area resulting in an increased number of publications. This study intends to use scientometric and bibliometric analysis to evaluate the research development and evolution of publication patterns in the field of mammalian microbiome between 2007 and 2020. 512 published articles were retrieved from the Web of Science Core Collection and were analyzed. We assessed the quantity and quality of research output through statistical methods of bibliometric indicators, comprising a number of publications, citations, productive authors, journals and countries, using a bibliometric analysis. Scientometric analysis was performed using main path analysis, bibliometric coupling, co-word co-occurrence and co-author analysis, systematically characterizing and visualizing the trend and delivering a pivotal review of the mammalian microbiome research status quo. The results identified an increase in the number of publications over time showing the rapid research growth, with top productive countries recording the highest number of research outcomes with influential research. The bibliographic coupling revealed the most shared papers that form landmark papers and the co-author analysis indicated the most influential authors in mammalian microbiome research. The evolutionary path of the mammalian microbiome research was traced using the main path analysis identifying the milestone papers. The frequently occurred words were enumerated from co-word, co-occurrence networks. The information from this study could be a transcript for a comprehensive understanding of current mammalian microbiome research and can also direct the future and emerging trends in this research realm.
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Sedighi, Mehri. "Application of word co-occurrence analysis method in mapping of the scientific fields (case study: the field of Informetrics)." Library Review 65, no. 1/2 (2016): 52–64. http://dx.doi.org/10.1108/lr-07-2015-0075.
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Purpose – The purpose of this article is to investigate the use of word co-occurrence analysis method in mapping of the scientific fields with emphasis on the field of Informetrics. Design/methodology/approach – This is an applied study using scientometrics, co-word analysis and network analysis and its steps are summarised as follows: collecting the data related to the Informetrics field indexed in Web of Science (WOS) database, refining and standardising the keywords of the extracted articles from WOS and preparing a selected list of these keywords, drawing the word co-occurrence map in the Informetrics field and analysing of results. Findings – Based on the resulted maps the concepts such as information science, library, bibliometric analysis, innovation and text mining are the most widely used topics in the field of Informetrics. The co-word occurrence maps drawn at different periods show the changes and stabilities in the concepts related to the field of Informetrics. A number of topics such as “bibliometric analysis” are present in all years, whereas others such as “innovation” have disappeared. New topics emerge as a recombination of existing topics and in interaction with new (technological) developments. Originality/value – The results of these analytical studies can be used as a guide for determining research priorities in the scientific fields, and also for planning and management in academic institutions.
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Aditya Prakash. "Twitter Sentimental Analysis." International Journal for Modern Trends in Science and Technology 6, no. 12 (2020): 355–59. http://dx.doi.org/10.46501/ijmtst061266.
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Twitter sentiment analysis (TSA) provides the methods to survey public emotions about the products or events associated with them. Categorization of opinions through tweets involves a great scope of study and may yield interesting results and insights on public opinion and social behavior towards different events, services, product, geopolitical issues, situations and scenarios that concern mankind at large. These attributes are expressed explicitly through emoticons, exclamation, sentiment words and so on. In this paper, we introduce a word embedding (Word2Vec) technique obtained by unsupervised learning built on large twitter corpora, this process uses co-occurrence statistical characteristics between words in tweets and hidden contextual semantic interrelation
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Amarulloh, Reza Ruhbani, and Viqhi Aswie. "Bibliometric Analysis of Virtual Reality in Science Education over the Three Decades (1993-2023)." Science Education International 35, no. 3 (2024): 270–80. http://dx.doi.org/10.33828/sei.v35.i3.10.
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This study aims to conduct a bibliometric analysis of the use of virtual reality in science education over three decades (1993–2023). The method involved data from Google Scholar-indexed publications using Publish or Perish with keywords related to “virtual reality” and “education” over the past 3 decades. Nine hundred and eighty-six publications were obtained with a total of 131,130 citations with an average of 133 citations/paper and 4371 citations/year. The collected data were then screened to ensure its quality. Next, VOSviewer software was conducted to perform co-authorship and co-occurrence analysis. The results of the co-authorship analysis showed that there were 164 authors eligible to be visualized and divided into 88 clusters, indicating a high level of collaboration among authors in this field. Co-occurrence analysis shows that “virtual reality” has an occurrence of 696 in cluster 5 and “science education” is only 16 in cluster 2 with word networks formed only on the words “virtual reality,” “education,” and “field.” This study emphasizes the need for better VR in science education and more research on its impact on students’ science literacy. It outlines VR usage trends in scienceeducation, informing future studies. The findings particularly highlight the importance of investigating VR’s effectiveness in formal educational settings.
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Xu, Weiran, and Koji Eguchi. "A supervised topic embedding model and its application." PLOS ONE 17, no. 11 (2022): e0277104. http://dx.doi.org/10.1371/journal.pone.0277104.
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We propose rTopicVec, a supervised topic embedding model that predicts response variables associated with documents by analyzing the text data. Topic modeling leverages document-level word co-occurrence patterns to learn latent topics of each document. While word embedding is a promising text analysis technique in which words are mapped into a low-dimensional continuous semantic space by exploiting the local word co-occurrence patterns within a small context window. Recently developed topic embedding benefits from combining those two approaches by modeling latent topics in a word embedding space. Our proposed rTopicVec and its regularized variant incorporate regression into the topic embedding model to model each document and a numerical label paired with the document jointly. In addition, our models yield topics predictive of the response variables as well as predict response variables for unlabeled documents. We evaluated the effectiveness of our models through experiments on two regression tasks: predicting stock return rates using news articles provided by Thomson Reuters and predicting movie ratings using movie reviews. Results showed that the prediction performance of our models was more accurate in comparison to three baselines with a statistically significant difference.
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Heo, Inseo, Jaeyon Lee, Junhyung Cha, and Namgi Han. "The Co-Occurrence Networks of the “World” in Korean Popular Song Lyrics." Korean Society of Culture and Convergence 44, no. 12 (2022): 69–85. http://dx.doi.org/10.33645/cnc.2022.12.44.12.69.
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The analysis of Korean popular song lyrics is helpful in investigating the social phenomenon or emotions of a certain age, but previous studies have only been limited to a small number of qualitative or quantitative analyses. To overcome this limitation, we collected 3,488 songs from 1964 to 2020 and created networks that link words in noun phrases and verb phrases appearing in the same line of lyrics. Then, we traced how the usage of the word “world” (“sesang” in Korean) changed during the periods from 1986 to 1997 and from 2015 to 2020 whereby to explore the world view of the masses diachronically. As a result, we found that the public in the past had a great interest in the change of the world and had a strong view of the world negatively, whereas recently, interest in the world itself decreased. The network data generated in this study will be made public, so anyone can access it through a web page.
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CROITORU, Ionut Marius, Nicoleta Daniela IGNAT, Paula Paraschiva SPIRIDON, Luciana DRAGOMIR, Maria Valerica ȘOLOIU, and Alexandra BRATILOVEANU. "BIBLIOMETRIC ANALYSIS OF INVESTMENT CLASSIFICATION CRITERIA." Management & Marketing 21, no. 2 (2023): 241–58. http://dx.doi.org/10.52846/mnmk.21.2.04.
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"In this paper, the scientific articles from the Web of Science base were analyzed, which address the investment classification criteria by means of the bibliometric analysis. Querying the Web of Science database and applying filters revealed a sample of 273 articles. The distribution by years, by countries, by authors, by categories, by fields of research, by sustainable development goals of this sample was followed. Also, within the work, a qualitative analysis was carried out, following the words and phrases with the highest density in the studied articles. The investigation method was based on the PRISMA Statements methodology. To determine the correlations regarding the countries of origin of the authors, the Co-authorship filter was used with a minimum number of 10 documents/country and a minimum number of 11 citations/country, so out of 73 countries only 14 met the conditions for analysis. For the word density analysis, the co-occurrence filter was applied, with a minimum occurrence of 5, resulting in 37 words grouped into 5 clusters. To support the conclusions, the following classifications were analyzed: WOS Categories, Research Areas, Sustainable Development Goals, Citation Topics Micro. The results of the research constitute the starting point for future analyzes in the field of investments."
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Soleimani, Behrouz Haji, and Stan Matwin. "Fast PMI-Based Word Embedding with Efficient Use of Unobserved Patterns." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7031–38. http://dx.doi.org/10.1609/aaai.v33i01.33017031.
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Continuous word representations that can capture the semantic information in the corpus are the building blocks of many natural language processing tasks. Pre-trained word embeddings are being used for sentiment analysis, text classification, question answering and so on. In this paper, we propose a new word embedding algorithm that works on a smoothed Positive Pointwise Mutual Information (PPMI) matrix which is obtained from the word-word co-occurrence counts. One of our major contributions is to propose an objective function and an optimization framework that exploits the full capacity of “negative examples”, the unobserved or insignificant wordword co-occurrences, in order to push unrelated words away from each other which improves the distribution of words in the latent space. We also propose a kernel similarity measure for the latent space that can effectively calculate the similarities in high dimensions. Moreover, we propose an approximate alternative to our algorithm using a modified Vantage Point tree and reduce the computational complexity of the algorithm to |V |log|V | with respect to the number of words in the vocabulary. We have trained various word embedding algorithms on articles of Wikipedia with 2.1 billion tokens and show that our method outperforms the state-of-the-art in most word similarity tasks by a good margin.
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Kim, Jeongin, Taekeun Hong, and Pankoo Kim. "Replacing Out-of-Vocabulary Words with an Appropriate Synonym Based on Word2VnCR." Mobile Information Systems 2021 (July 16, 2021): 1–7. http://dx.doi.org/10.1155/2021/5548426.
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The most typical problem in an analysis of natural language is finding synonyms of out-of-vocabulary (OOV) words. When someone tries to understand a sentence containing an OOV word, the person determines the most appropriate meaning of a replacement word using the meanings of co-occurrence words under the same context based on the conceptual system learned. In this study, a word-to-vector and conceptual relationship (Word2VnCR) algorithm is proposed that replaces an OOV word leading to an erroneous morphemic analysis with an appropriate synonym. TheWord2VnCR algorithm is an improvement over the conventional Word2Vec algorithm, which has a problem in suggesting a replacement word by not determining the similarity of the word. After word-embedding learning is conducted using the learning dataset, the replacement word candidates of the OOV word are extracted. The semantic similarities of the extracted replacement word candidates are measured with the surrounding neighboring words of the OOV word, and a replacement word having the highest similarity value is selected as a replacement. To evaluate the performance of the proposed Word2VnCR algorithm, a comparative experiment was conducted using the Word2VnCR and Word2Vec algorithms. As the experimental results indicate, the proposed algorithm shows a higher accuracy than the Word2Vec algorithm.
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Martelo Gómez, Raúl José, Piedad Mary Martelo Gómez, and David Antonio Franco Borré. "Bibliometric Analysis of Blockchain Technology in Finance." Tecnura 28, no. 80 (2024): 83–98. https://doi.org/10.14483/22487638.19955.
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Context: This research addresses one of the technologies that have been successfully applied in the financial sector: Blockchain. While this technology is not yet widely adopted in finance, an increasing number of proof-of-conceptprojects are being conducted to get closer to its massive implementation. This highlights the importance of studying Blockchain technology in finance. Therefore, the aim of this research is to conduct a bibliometric study that evaluates the advances in the literature from objective aspects.Methodology: The present study is classified as quantitative and descriptive because it analyzes the scientific production related to Blockchain technology in finance. Bibliometrics were applied in order to demonstrate the progress of scientific production on Blockchain technology in finance, using data registered in the Scopus database.Results: Quantitative and qualitative results were obtained in which aspects such as the source, subject area, the number of documents published per year, country, and the most prolific authors were evaluated, in addition to an analysis of word co-occurrence and co- authorship. A total of 596 publications were identified with which a bibliometric analysis was carried out and the free software VOSviewer was used to examine the different maps of co-authorship and word co-occurrence networks.Conclusions: It is concluded that publications are scarce compared to other technologies that are part of the digital revolution. However, proof-of-concept projects in various financial services are increasing, making it imperative to continue researching their applications, regulation, and risks. As a result, scientific production is expected to grow.
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Andreopoulos, Bill, Dimitra Alexopoulou, and Michael Schroeder. "Word Sense Disambiguation in biomedical ontologies with term co-occurrence analysis and document clustering." International Journal of Data Mining and Bioinformatics 2, no. 3 (2008): 193. http://dx.doi.org/10.1504/ijdmb.2008.020522.
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Liu, Liu, and Ruisheng Tan. "An Analysis of Antonymy Co-occurrence Four-character Idiom: Concentrating on Word Formation Morpheme." Korea Journal of Chinese Linguistics 103 (December 31, 2022): 149–84. http://dx.doi.org/10.38068/kjcl.103.6.
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Tungseth, Mai. "Interactions of particles, adjectival resultatives and benefactive double object constructions in Norwegian." Nordic Journal of Linguistics 30, no. 2 (2007): 209–28. http://dx.doi.org/10.1017/s033258650700176x.
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In this paper, I take a closer look at a set of observations concerning word order and co-occurrence restrictions on verb-particle constructions, benefactive double object constructions and resultative constructions in Norwegian. While a particle can co-occur with both a beneficiary DP and a resultative AP, beneficiary DPs and resultatives cannot co-occur at all. I give an analysis in terms of the system proposed in Ramchand (2006), where I argue that the co-occurrence restrictions follow from the syntactic structure assumed together with independent properties of adjectival resultative constructions and verb-particle constructions.
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Sánchez, Pérez Manuel, Yépez Eduardo Terán, Carrillo María Belén Marín, and López Nuria Rueda. "40 years of sharing economy research: An intellectual and cognitive structures analysis." International Journal of Hospitality Management 94, April (2021): 102856. https://doi.org/10.5281/zenodo.13341768.
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This study aims to analyze the intellectual and cognitive structures of the sharing economy as a field of research. Adopting an integrated bibliometric approach of citation, co-citation, and co-word analysis, this study analyses 941 articles published on Web of Science from 1978 to 2019. Findings reveal that despite there being a latent concentration in citations distribution, the ascending and descending influence patterns discovered over time indicate a dynamic flow and healthy growth of the field. The analysis of the intellectual structure identifies four main areas of research, with hospitality and tourism being the most developed, and the journals about hospitality being the preferred channel for research into the sharing economy. Finally, for the cognitive structure analysis, in-depth strategic diagrams, thematic evolution, and trend analysis disclose some research gaps. Thus, we contribute to the sharing economy literature by inductively synthesizing, and organizing SE research, and by proposing future research directions.
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Ramlee, Nur Atiqah Zakiyyah, Lina Nadia Abd Rahim, Hardy Loh Rahim, and Nursaadatun Nisak Ahmad. "Bibliometric Analysis of Technopreneurship Research in Scopus Database." Information Management and Business Review 15, no. 3(SI) (2023): 359–68. http://dx.doi.org/10.22610/imbr.v15i3(si).3492.
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Post Covid-19 outbreak has shown a growing trend in technology adoption. Instantaneously, the research on technology entrepreneurship has surged to shed light on the impact of this research on the national economy. This research aims to explore research papers specifically research journal articles on technopreneurship that were published in the Scopus database. 489 and 106 documents were found in the Scopus database on the query of technology entrepreneurship and technopreneurship and were extracted to VOSviewer for data visualization. Key themes were identified using co-word or co-occurrence analysis, and relevant future study directions were demonstrated.
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Kim, Taejoon, and Haiyan Wang. "Matrix Factorization and Prediction for High-Dimensional Co-Occurrence Count Data via Shared Parameter Alternating Zero Inflated Gamma Model." Mathematics 12, no. 21 (2024): 3365. http://dx.doi.org/10.3390/math12213365.
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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.
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Liu, Mengque, Xia Zou, Jiyin Chen, and Shuangge Ma. "Comparative Analysis of Social Support in Online Health Communities Using a Word Co-Occurrence Network Analysis Approach." Entropy 24, no. 2 (2022): 174. http://dx.doi.org/10.3390/e24020174.
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Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with others facing similar health problems and receive multiple types of social support, including but not limited to informational support, emotional support, and companionship. The aim of this study is to examine the differences in social support communication among people with different types of cancers. A novel approach is developed to better understand the types of social support embedded in OHC posts. Our approach, based on the word co-occurrence network analysis, preserves the semantic structures of the texts. Information extraction from the semantic structures is supported by the interplay of quantitative and qualitative analyses of the network structures. Our analysis shows that significant differences in social support exist across cancer types, and evidence for the differences across diseases in terms of communication preferences and language use is also identified. Overall, this study can establish a new venue for extracting and analyzing information, so as to inform social support for clinical care.
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Liao, Yuqing, and Jingliang Chen. "Research on China’s Green Finance Policies Based on Text Mining." E3S Web of Conferences 185 (2020): 02024. http://dx.doi.org/10.1051/e3sconf/202018502024.
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Based on the green finance policies in China from 2017 to 2019, this paper extracts feature and high-frequency words from policy documents, uses word cloud diagram, co-occurrence matrix and social network analysis techniques to quantitatively analyse the information contained in the green finance policies over the past three years and highlights the hot issues in question, thus providing a multi-layered and wideranging pathway for facilitating the orderly development of green finance industries across China.
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Nishida, Yasushi, and Katsuhiro Honda. "Visualization of Potential Technical Solutions by SOM and Co-Clustering and its Extension to Multi-View Situation." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 1 (2020): 65–72. http://dx.doi.org/10.20965/jaciii.2020.p0065.
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In order to support inspiration of potential technical solutions, this paper considers visualization of solving means varied in patent documents through SOM. Non-structured patent document data can be quantified through two different scheme: word level co-occurrence probability vectors and correlation coefficients of the generated co-occurrence probability vectors. Comparing the two SOMs derived with the above schemes is useful for supporting innovation acceleration through extraction of important pairs of related factors in new technology development. In this paper, co-cluster structures are utilized for emphasizing field-related solutions by constructing multiple SOMs after co-clustering. Document × keyword co-occurrence analysis achieves extraction of co-clusters consisting of mutually related pairs in particular fields. Additionally, this paper also considers an extension to a multi-view situation, where each patent is characterized by additional patent classification system of F-term by Japan Patent Office. Through multi-view co-clustering among documents × keywords × F-terms, theme field-related knowledge is demonstrated to be extracted.
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Lim, Jong Geon. "Co-occurrence Word Patterns for ‘Urban Administration’ and ‘Urban Policy’: Focused on Newspaper Article Analysis." Journal of the Korean Urban Management Association 38, no. 1 (2025): 89–98. https://doi.org/10.36700/kruma.2025.3.38.1.89.
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Vu Thi Thuy Hang and Nguyen Thi Van. "Mapping the research of digital transformation in agriculture." VNU University of Economics and Business 5, no. 2 (2025): 118. https://doi.org/10.57110/vnu-jeb.v5i2.322.
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This study aims to systematically map the rapidly growing research landscape on Digital Transformation in Agriculture (DTA). A bibliometric analysis was conducted on 96 documents related to DTA, sourced from the Scopus database. The research methodology encompasses co-occurrence keyword and co-citation analyses, focusing on 2019 to 2023. The study reveals a significant annual increase in the volume of publications, with Russia emerging as the leading contributor. The co-word analysis identifies three dominant research themes, characterized by 17 keywords with a minimum occurrence of five times. The clusters are innovation and agrifood-tech, sustainable agricultural development and digital economy, digitalization of agriculture, and Russia. The co-citation analysis for cited authors created a network of four clusters of innovation efforts in agriculture, information systems on farms, the role of business models and dynamic capabilities in sustainable intensification, and the challenges, opportunities, and sustainability of DTA. The findings indicate that research on DTA is still developing, with significant research gaps remaining. This study aims to contribute to the field's academic literature and practical applications.
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Kang, Kyung-Ah, Suk Jung Han, Jiyoung Chun, and Hyun-Yong Kim. "Research trends related to childhood and adolescent cancer survivors in South Korea using word co-occurrence network analysis." Child Health Nursing Research 27, no. 3 (2021): 201–10. http://dx.doi.org/10.4094/chnr.2021.27.3.201.
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Purpose: This study analyzed research trends related to childhood and adolescent cancer survivors (CACS) using word co-occurrence network analysis on studies registered in the Korean Citation Index (KCI).Methods: This word co-occurrence network analysis study explored major research trends by constructing a network based on relationships between keywords (semantic morphemes) in the abstracts of published articles. Research articles published in the KCI over the past 10 years were collected using the Biblio Data Collector tool included in the NetMiner Program (version 4), using "cancer survivors", "adolescent", and "child" as the main search terms. After pre-processing, analyses were conducted on centrality (degree and eigenvector), cohesion (community), and topic modeling.Results: For centrality, the top 10 keywords included "treatment", "factor", "intervention", "group", "radiotherapy", "health", "risk", "measurement", "outcome", and "quality of life". In terms of cohesion and topic analysis, three categories were identified as the major research trends: "treatment and complications", "adaptation and support needs", and "management and quality of life".Conclusion: The keywords from the three main categories reflected interdisciplinary identification. Many studies on adaptation and support needs were identified in our analysis of nursing literature. Further research on managing and evaluating the quality of life among CACS must also be conducted.
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Martí-Sánchez, Myriam, Desamparados Cervantes-Zacarés, and Arturo Ortigosa-Blanch. "Entrepreneurship in the digital press: a semantic analysis." International Journal of Entrepreneurial Behavior & Research 26, no. 3 (2019): 416–31. http://dx.doi.org/10.1108/ijebr-06-2019-0394.
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Purpose The purpose of this paper is to analyse how the media addresses entrepreneurship and to identify the attributes linked to this phenomenon. Design/methodology/approach The sample is defined in terms of a linguistic corpus comprised of content related to entrepreneurship drawn from the digital editions of the three most important Spanish economic newspapers for the period 2010–2017. Word association and co-occurrence analyses were carried out. Further, a non-supervised clustering process was used as the basis for a thematic analysis. Findings Correspondence between social and media patterns related to the entrepreneurship phenomenon is revealed by the results. It is shown how attributes such as “success”, “innovation”, “ecosystem” and “woman” appear as very relevant and are linked to different co-occurrence scenarios. Relevant thematic groups are also identified related to lexical associations such as innovation, digital economy and public policies linked to entrepreneurship. Research limitations/implications It is important to emphasise that this study has identified and explored relationships between words, but not their evolution. Furthermore, conclusions cannot be drawn concerning whether there are differences in how each newspaper has dealt with entrepreneurship because of the way the corpus was constructed. Originality/value The study provides empirical evidence that helps to identify the way media approaches entrepreneurship. The authors carried out the analysis on the media contents and not on the perception of the public on the phenomenon.
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Xia, Huosong, Yuting Meng, Wuyue An, Zixuan Chen, and Zuopeng Zhang. "Feature mining and analysis of gray privacy products." Information Discovery and Delivery 48, no. 2 (2020): 67–78. http://dx.doi.org/10.1108/idd-09-2019-0063.
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Purpose Excavating valuable outlier information of gray privacy products, the purpose of this study takes the online reviews of women’s underwear as an example, explores the outlier characteristics of online commentary data, and analyzes the online consumer behavior of consumers’ gray privacy products. Design/methodology/approach This research adopts the social network analysis method to analyze online reviews. Based on the online reviews collected from women’s underwear flagship store Victoria’s Secret at Tmall, this study performs word segmentation and word frequency analysis. Using the fuzzy query method, the research builds the corresponding co-word matrix and conducts co-occurrence analysis to summarize the factors affecting consumers’ purchase behavior of female underwear. Findings Establishing a formal framework of gray privacy products, this paper confirms the commonalities among consumers with respect to their perceptions of gray privacy products, shows that consumers have high privacy concerns about the disclosure or secondary use of personal private information when shopping gray privacy products, and demonstrates the big difference between online reviews of gray privacy products and their consumer descriptions. Originality/value The research lays a solid foundation for future research in gray privacy products. The factors identified in this study provide a practical reference for the continuous improvement of gray privacy products and services.
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Han, Ja-Young, Young-Eun Seo, Jae-Hee Kwon, Jae Hyun Kim, and Myeong Gyu Kim. "Cardioprotective Effects of PARP Inhibitors: A Re-Analysis of a Meta-Analysis and a Real-Word Data Analysis Using the FAERS Database." Journal of Clinical Medicine 13, no. 5 (2024): 1218. http://dx.doi.org/10.3390/jcm13051218.
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Objective: This study aimed to assess the potential of PARP inhibitors to prevent cardiotoxicity. Methods: First, a re-analysis and update of a previously published study was conducted. Additional searches were conducted of the PubMed and Cochrane Central Register of Controlled Trials databases on 2 June 2023. After the selection process, the pooled odds ratio (OR) for cardiac adverse events (AEs) was calculated. Second, the FAERS database was examined for 10 frequently co-administered anticancer agents. The reporting odds ratio (ROR) was calculated based on the occurrence of cardiac AEs depending on the co-administration of PARP inhibitors. Results: Seven studies were selected for the meta-analysis. Although not statistically significant, co-administration of PARP inhibitors with chemotherapy/bevacizumab decreased the risk of cardiac AEs (Peto OR = 0.61; p = 0.36), while co-administration with antiandrogens increased the risk of cardiac AEs (Peto OR = 1.83; p = 0.18). A total of 19 cases of cardiac AEs were reported with co-administration of PARP inhibitors in the FAERS database. Co-administration of PARP inhibitors with chemotherapy/bevacizumab significantly decreased the risk of cardiac AEs (ROR = 0.352; 95% confidence interval (CI), 0.194–0.637). On the other hand, for antiandrogens co-administered with PARP inhibitors, the ROR was 3.496 (95% CI, 1.539–7.942). The ROR for immune checkpoint inhibitors co-administered with PARP inhibitors was 0.606 (95% CI, 0.151–2.432), indicating a non-significant effect on cardiac AEs. Conclusion: This study reports that PARP inhibitors show cardioprotective effects when used with conventional anticancer agents.
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Addawiyah, Ida Roosyidah, I. Made Astra, Esmar Budi, and Firmanul Catur Wibowo. "Multiple Representations in Physics Learning: A Bibliometric Analysis." JIPF (Jurnal Ilmu Pendidikan Fisika) 9, no. 2 (2024): 277. http://dx.doi.org/10.26737/jipf.v9i2.4907.
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The application of multiple representation approach in physics learning has been adopted by several researchers, especially to improve the ability and achieve effective learning. The use of multiple representation approach in physics learning over the past few years has provided good results on student learning outcomes, including being able to improve and develop abstract physics concepts, scientific complex skills, metacognition skills, and problem solving. This research is the result of a review based on bibliometric analysis of multiple representations in physics learning. Data was obtained based on Scopus sources from 2013 to 2023, then obtained data as many as 121 articles which then the data was processed using RStudio and VOSViewer. Based on the results of the analysis using Rstudio and VOSViewer, it also displays other information such as year, country, author, institution, journal, co-word analysis, co-occurrence network, and visual density on occurrence. The results of this study provide a framework of the current trends of multiple representations in physics learning both in the present and some indicators that can be used as opportunities for future research.
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Nor Paizin, Muhsin, Siti Maziah Ab Rahman, Khalid Abdul Wahid, Mohd Noor Azam Nafi, Suryani Awang, and Mariam Setapa. "Bibliometric Analysis of Zakat Research in Scopus Database." International Journal of Zakat 6, no. 1 (2021): 13–24. http://dx.doi.org/10.37706/ijaz.v6i1.253.
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Scopus research paper on the zakat was systematically analyzed using the VOSviewer bibliometric measurement. A total of 492 citation data was exported from Scopus on the query of Zakat, and from the initial result, twelve journals were selected in the expanded query process. The journals are Journal of Islamic Accounting and Business Research, International Journal of Islamic and Middle Eastern Finance and Management, International Journal of Innovation Creativity and Change, Advanced Science Letters, and Iop Conference Series Earth and Environmental Science were selected in the query expansion and exported for data visualization in VOSviewer. Results from the journal query returned 492 documents specializing in research of zakat payment. Co-word or co-occurrence analysis was used to identify key themes, and potential future research direction was highlighted.
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Saleh, Abdul, and Sri Rahayu. "Peta hubungan topik penelitian skripsi IPB University lulusan tahun 2020." Berkala Ilmu Perpustakaan dan Informasi 18, no. 2 (2022): 174–88. http://dx.doi.org/10.22146/bip.v18i2.3420.
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Introduction. Agricultural progress cannot be separated from research activities carried out by researchers and students. It is suspected that the thesis research topics in IPB undergraduate theses are thought to be related to one another. This study aims to determine the relationship of the research topics of the IPB undergraduate theses in 2020.
 Research methods. This research is a mixed method research. The data collected by purposive sampling technic with a 2020 limit. The data was retieved from IPB repocitory.
 Data analysis. The data were tabulated and analyzed using the VOSviewer to generate maps relationships between keywords.
 Results and Discussion. Co-word analysis of controlled keywords found 181 keywords. Co-word analysis with a limitation of three keyword occurrences found 120 related keywords grouped into 10 clusters. Co-word analysis of the keywords formed by the author found 10,943 keywords. The ten occurrence limit found 81 keywords in 11 clusters. Similar research topics were found in at least two faculties.
 Conclusion. The research topics of IPB's thesis were spread into 120 topics with 10 clusters having close relationships. It was found that there were similarities in research topics between one faculty and another.
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Tsay, Ming-Yueh, Yu-Wei Tseng, and Jia-Hsuan Lin. "Knowledge domain analysis of artificial intelligence in automation and control systems." COLLNET Journal of Scientometrics and Information Management 18, no. 1 (2024): 45–61. http://dx.doi.org/10.47974/cjsim-2023-0013.
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This study mainly explores the evolution in the subject development of artificial intelligence documents in the field of automation and control systems from 1956 to 2019. Based on the search in WoS SCIE database, 27,782 documents are revealed. The VOSviewer is then employed to draw the co-citation network diagram. Moreover, both VOSviewer and Citespace are used to draw the keyword co-occurrence network diagram and the burst word change diagram from 2000 to 2019 to master the field trend. The results from the keyword co-occurrence reveal that “neural network” and “adaptive control” are the main keywords in all periods and after 2010 , the research topics has shifted to “adaptive control”, especially in Lyapunov method, nonlinear control system, control system synthesis and multi-agent system. The overall document co-citation network can be roughly divided into two research axes: “adaptive control” and “nonlinear system”. In terms of author cocitation, Ge, Shuzhi Sam, Li, Zhijun, He, Wei are the most commonly cited high-productivity authors, while Lewis, Frank L. is at the center of the whole co-citation network and plays a leading role in the field. In terms of journal co-citation, Automatica and IEEE Transactions on Automatic Control are most often cited together with other journals, and the link strength between the two is strong. These two journals are of small number but high-quality publications. IEEE Transactions on Industrial Electronics is at the center of the co-citation network, which shows its multidisciplinary nature.
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Cong, Jin, and Haitao Liu. "Linguistic emergence from a networks approach: The case of modern Chinese two-character words." PLOS ONE 16, no. 11 (2021): e0259818. http://dx.doi.org/10.1371/journal.pone.0259818.
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The models of linguistic networks and their analytical tools constitute a potential methodology for investigating the formation of structural patterns in actual language use. Research with this methodology has just started, which can hopefully shed light on the emergent nature of linguistic structure. This study attempts to employ linguistic networks to investigate the formation of modern Chinese two-character words (as structural units based on the chunking of their component characters) in the actual use of modern Chinese, which manifests itself as continuous streams of Chinese characters. Network models were constructed based on authentic Chinese language data, with Chinese characters as nodes, their co-occurrence relations as directed links, and the co-occurrence frequencies as link weights. Quantitative analysis of the network models has shown that a Chinese two-character word can highlight itself as a two-node island, i.e., a cohesive sub-network with its two component characters co-occurring more frequently than they co-occur with the other characters. This highlighting mechanism may play a vital role in the formation and acquisition of two-character words in actual language use. Moreover, this mechanism may also throw some light on the emergence of other structural phenomena (with the chunking of specific linguistic units as their basis).
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Shams, Mohammadreza, and Ahmad Baraani-Dastjerdi. "Enriched LDA (ELDA): Combination of latent Dirichlet allocation with word co-occurrence analysis for aspect extraction." Expert Systems with Applications 80 (September 2017): 136–46. http://dx.doi.org/10.1016/j.eswa.2017.02.038.
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Wang, Xueying. "Bibliometric Analysis on Global Comparative Literature Research." Scholars International Journal of Linguistics and Literature 5, no. 9 (2022): 303–9. http://dx.doi.org/10.36348/sijll.2022.v05i09.009.
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This article aims to analyze the research trend and hotspots in the field of comparative literature by using the method of bibliometrics. The data is derived from the Web of Science Core Collection Database. The visualization software VOSViewer is utilized to draw keyword co-occurrence knowledge graph. R programming language is employed to analyze the quantity of publications, core journals, highly cited papers, the most contributing authors, and keyword word cloud. The results indicate that ever since 1975, this study field has entered a period of rapid development; top journals with most publications are mainly from France, the United States, the United Kingdom and Canada. Most of the highly cited articles have emerged in the recent two decades, and quite a few of them inherit the academic tradition of adopting a geographic perspective. The keyword word cloud and the keyword co-ocurrence knowledge mapping reveal that comparative literature study is shifting its focus from literary history and intertextuality to identity, culture, literary theory and world literature. The recent reserch hotspots in this field are mainly identity, culture, world literature, literary theory, Latin American literature, appropriation, genre, theatre, ethics and digital humanities, etc.
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Qian, Yu, Xunjie Gou, and Zeshui Xu. "The Development and Progress of Engineering Economics: A Retrospect and Prospect Based on Visual Analysis." Engineering Economics 35, no. 1 (2024): 4–24. http://dx.doi.org/10.5755/j01.ee.35.1.32448.
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Engineering economics is a cross subject with a wide range of applications, and it has taken on different characteristics with the changing times. The aim of this paper is to depict a sufficiently elaborate and vivid knowledge map of this field, with further discussion and outlook on the hotspots presented therein. Based on the principles and methods of bibliometrics, we use several visualization tools, mainly Vosviewer, to present the characteristics of the published literature within the field of engineering economics from multiple perspectives. Specifically, we collect 624 engineering economics documents published in the Web of Science core collection database between 1915 and 2021, and quantitatively analyze them in the following three aspects: (1) basic data characteristics, including annual publications, annual citations, research directions, and highly cited publications; (2) outstanding performers and cooperations in the four levels of country/region, institution, source and author, including co-authorship, bibliographic coupling, co-citation and co-occurrence analyses; and (3) keyword analyses, including co-occurrence analyses, burst detection analyses, and high-frequency word clouds. In addition, we further explore important topics within the field represented by intelligent and green transformation.
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Pstyga, Alicja. "Składnia derywatów w tekście: kompozycja i dekompozycja struktur złożonych oryginału w kontekście doboru odpowiedników w przekładzie (na materiale rosyjskich i polskich tekstów prasowych)." Slavia Meridionalis 13 (May 1, 2015): 171–84. http://dx.doi.org/10.11649/sm.2013.009.
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The syntax of compound words in texts: Composition and decomposition of compound words in view of the selection of their equivalents in the translation of Russian press articles into PolishThe starting point for the analysis is the broad interpretation of syntax proposed by Stanisław Karolak. We should take into consideration his findings, concerning combinatory rules and complicated relationships in compound words. The decomposition of these words – even violating the rules of concept co-occurrence – allows us to uncover their proper semantic interpretation. Karolak claims that in Slavic languages, the rules of word formation enable simple expressions to function in utterances which are more complicated than simple sentences.The aim of this paper is to present the functioning of compound words in Russian press articles from the translation perspective. One of the most interesting examples is the Russian compound word евронадежды (with its Polish equivalent europazerni), used in a text about problems with accommodation during the 2012 UEFA European Football Championship in Ukraine.
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Nazar, Rogelio. "Distributional analysis applied to terminology extraction." Terminology 22, no. 2 (2016): 141–70. http://dx.doi.org/10.1075/term.22.2.01naz.
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This paper presents the first results of a new method for terminology extraction based on distributional analysis. The intuition behind the algorithm is that single or multi-word lexical units that refer to specialised concepts will show a characteristic co-occurrence pattern, described as a tendency to appear in the same contexts with other conceptually related terms. E.g. the term fluoxetine will systematically appear in the same sentences with other related terms such as depression, serotonin reuptake inhibitor, obsessive–compulsive disorder and others. Of course, terms will co-occur with general vocabulary units as well, but not with a characteristic pattern as when a conceptual relation holds. Experimental evaluation of this method was conducted in a corpus of psychiatry journals from Spain and Latin America, and concluded that the results are significantly better than other methods.
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Herzallah, Doaa Am. "The Influence of Electronic Word-of-Mouth in Business Research: Identifying Main Topics and Actors." Journal of Business 16, no. 1 (2025): 177–213. https://doi.org/10.21678/jb.2025.2452.
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Purpose: The primary goal of this research is to do a systematic review from 2012 to 2022, investigating how the electronic word of mouth is interconnected and their overall research influence. Design/Methodology/Approach: Visualize scientific production employing the Web of Science database and VOS viewer. This study analysed 1449 papers (2012-2022) in Business Economics, identifying influential authors, areas, countries, and journals Co-occurrence of keywords, co-authorship, citation, bibliographic coupling, and co-citation analysis. Findings: This research shed light on the specific aspects of electronic that have gained significant attention and investigation, as well as the global distribution of scholarly contributions, suggesting new areas for further exploration. The main results also reveal that electronic word of mouth is one of the most important topics in Business, Management, and Marketing research. This study bridges knowledge gaps by conducting a thorough thematic evaluation and using systematic analysis to provide a more comprehensive picture of electronic word-of-mouth research across the years. The results provide insight into the development of electronic word of mouth and open up new avenues for study in this area. Originality: Researchers are captivated by the rising significance of Electronic Word-of-Mouth (E-WOM) in the digital age because of its multidisciplinary nature, practical implications for marketing strategies, and dynamic interaction with technology. Thus, a thorough analysis of electronic word-of-mouth research is required, in addition to covering themes from the past, present, and future, studying new aspects of electronic word-of-mouth.
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Kuperberg, Gina R., Martin Paczynski, and Tali Ditman. "Establishing Causal Coherence across Sentences: An ERP Study." Journal of Cognitive Neuroscience 23, no. 5 (2011): 1230–46. http://dx.doi.org/10.1162/jocn.2010.21452.
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This study examined neural activity associated with establishing causal relationships across sentences during on-line comprehension. ERPs were measured while participants read and judged the relatedness of three-sentence scenarios in which the final sentence was highly causally related, intermediately related, and causally unrelated to its context. Lexico-semantic co-occurrence was matched across the three conditions using a Latent Semantic Analysis. Critical words in causally unrelated scenarios evoked a larger N400 than words in both highly causally related and intermediately related scenarios, regardless of whether they appeared before or at the sentence-final position. At midline sites, the N400 to intermediately related sentence-final words was attenuated to the same degree as to highly causally related words, but otherwise the N400 to intermediately related words fell in between that evoked by highly causally related and intermediately related words. No modulation of the late positivity/P600 component was observed across conditions. These results indicate that both simple and complex causal inferences can influence the earliest stages of semantically processing an incoming word. Further, they suggest that causal coherence, at the situation level, can influence incremental word-by-word discourse comprehension, even when semantic relationships between individual words are matched.
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Mahmoud, Adnen, and Mounir Zrigui. "Semantic Similarity Analysis for Corpus Development and Paraphrase Detection in Arabic." International Arab Journal of Information Technology 18, no. 1 (2020): 1–7. http://dx.doi.org/10.34028/iajit/18/1/1.
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Paraphrase detection allows determining how original and suspect documents convey the same meaning. It has attracted attention from researchers in many Natural Language Processing (NLP) tasks such as plagiarism detection, question answering, information retrieval, etc., Traditional methods (e.g., Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Latent Semantic Analysis (LSA)) cannot capture efficiently hidden semantic relations when sentences may not contain any common words or the co-occurrence of words is rarely present. Therefore, we proposed a deep learning model based on Global Word embedding (GloVe) and Recurrent Convolutional Neural Network (RCNN). It was efficient for capturing more contextual dependencies between words vectors with precise semantic meanings. Seeing the lack of resources in Arabic language publicly available, we developed a paraphrased corpus automatically. It preserved syntactic and semantic structures of Arabic sentences using word2vec model and Part-Of-Speech (POS) annotation. Overall experiments shown that our proposed model outperformed the state-of-the-art methods in terms of precision and recall
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Chung, Kevin Kien Hoa, and Chun Bun Lam. "Cognitive-Linguistic Skills Underlying Word Reading and Spelling Difficulties in Chinese Adolescents With Dyslexia." Journal of Learning Disabilities 53, no. 1 (2019): 48–59. http://dx.doi.org/10.1177/0022219419882648.
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The present study investigated the co-occurrence of word reading and spelling difficulties for Chinese first language (L1) and English second language (L2) and the role of morphological awareness in word reading and spelling ability across two languages. A total of 110 Hong Kong Chinese-speaking students in Grade 7, including 55 adolescents with dyslexia (28 males, mean age = 152.11 months) and 55 typically developing adolescents (27 males, mean age = 151.85 months) participated. They were assessed on the cognitive-linguistic measures of morphological awareness, phonological awareness, vocabulary knowledge, rapid naming, word reading, and word spelling in L1 and L2. Multivariate analysis of variance showed that compared with the typical students, adolescents with dyslexia had poorer performance in all L1 and L2 measures except the phonological awareness in Chinese. Hierarchical regression analysis indicated that for both groups of students, morphological awareness contributed uniquely to word reading and spelling in L1 and L2; rapid letter naming contributed uniquely to English word spelling. Findings highlight the importance of co-occurring difficulties in L1 and L2 reading and spelling and that morphological awareness may play a critical role in predicting word reading and spelling across languages for Chinese adolescents with dyslexia and those without difficulty.
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Espasandin-Bustelo, Francisco, Beatriz Palacios-Florencio, and Javier Sánchez-Rivas García. "CSR intellectual structure in management and tourism." TQM Journal 32, no. 3 (2020): 521–41. http://dx.doi.org/10.1108/tqm-06-2019-0173.
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PurposeCorporate social responsibility (CSR) research intellectual structures are analysed and compared on the basis of the main international journals of management and tourism.Design/methodology/approachDocument co-citation, author co-citation and word co-occurrence are carried out using UCINET and NODEXL, software for social network analysis (SNA).FindingsDifferences and similarities between both research fields are provided, study limitations are pointed out and future research lines are suggested.Originality/valueThe main works concerning the topic of CSR are identified for each area of knowledge management and tourism. These are the basis for constructing the corresponding knowledge, and co-citation patterns among them are shown graphically.
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Eduljee, Nina B., Karen Croteau, Rupak Chakravarty, and Laurie Murphy. "Understanding Research Trends in HyFlex (hybrid flexible) Instruction Model: A Scientometric Approach." International Journal of Instruction 15 (October 5, 2022): 935–54. https://doi.org/10.5281/zenodo.10996501.
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The onset of the Covid-19 pandemic necessitated that higher education institutions go online and utilize a HyFlex instruction model. The current study used a scientometric approach to evaluate the current status of HyFlex, as well as a visual analysis of the topic. Published research from 1989-2021 was retrieved from Web of Science (WoS) and the search generated 1453 results, which were analysed by title, year of publication, authors, country, journal, and research area. The data was processed using VOSviewer and Bibliometrix R software to visualize trends for HyFlex. The research identified document types, author collaborations, annual scientific production, most relevant journals, collaboration network between authors, institutions, country, cluster coupling of authors, documents and sources, thematic evolution, and co-occurrence of all keywords. The results indicated the topic gained interest in 2008, with the highest number of articles published in 2019-2020. The top collaborator and country with the highest volume of citations and published articles was the United States. Word clusters indicated the most repetitive words were students, education, performance, and knowledge. The visualization of data offers information on trends on the body of research as well as providing researchers an understanding of the topic.
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Yano, Yuki, David Blandford, Atsushi Maruyama, and Tetsuya Nakamura. "Consumer perceptions of fresh leafy vegetables in Japan." British Food Journal 120, no. 11 (2018): 2554–68. http://dx.doi.org/10.1108/bfj-09-2017-0500.
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Purpose The purpose of this paper is to investigate Japanese consumer perceptions of the benefits of consuming fresh leafy vegetables. Design/methodology/approach An online bulletin board survey was conducted in Japan to collect responses to an open-ended question about reasons for consuming fresh leafy vegetables. A total of 897 responses were analysed using word co-occurrence network analysis. A community detection method and centrality measures were used to interpret the resulting network map. Findings Using a community detection algorithm, the authors identify six major groups of words that represent respondents’ core motives for consuming leafy vegetables. While Japanese consumers view health benefits to be most important, sensory factors, such as texture, colour, and palatability, and convenience factors also influence attitudes. The authors find that centrality measures can be useful in identifying keywords that appear in various contexts of consumer responses. Originality/value This is the first paper to use a quantitative text analysis to examine consumer perceptions for fresh leafy vegetables. The analysis also provides pointers for creating visually interpretable co-occurrence network maps from textual data and discusses the role of community structure and centrality in interpreting such maps.