Word Clusters Research Articles

Exploring Coherence among Sri Lankan CFL Learners in Chinese-English Translation: Decoding and Interpreting of Culture-loaded Content

Modern translation has shifted from its traditional approach of merely translating linguistic features towards a more culturally sensitive approach, which further considers the sociolinguistic spaces of the source text in producing the target text. This significant theoretical transformation has demanded the translator to play a more comprehensive role, which involves intercultural and sociolinguistic competencies apart from language competence. Subsequently, maintaining cohesion and coherence within and among texts also demands a heavier effort from the translator with neologisms being created frequently. The present study has been an attempt to identify the core issues pertaining to lexical coherence in translating from Chinese sources in Sri Lankan context using sample texts translated by Sri Lankan CFL (Chinese as a Foreign Language) learners. 20 culture-loaded lexical items were examined for coherence using a plagiarism checker and compared with Google and Baidu translation outputs of the source text. The results manifest that there is significant coherence among the target texts but coherence is a result of excessive use of machine translation, especially Google translate(?=94.67). 5-10 clusters were the most frequent clusters with an average of 21.73 clusters. < 10 word clusters have recorded an average of 19.07 whereas> 5 word clusters have recorded an average of 12.60.The highest number of clusters is recorded in text 5 with 44 clusters of <10 words. The highest number of translated variants of the same cultural term of the selected word list (n=20) is 4 while 70% (n=14) of the selected terms were only translated into either 1 or 2 variants. While machine translation is perceived as a constructive instrument under proper management, vigilant use of footnotes and end notes is perceived as a viable solution for reducing misinterpretation, vagueness and confusion in translating culture-loaded lexical items.

Measuring the applicability of user-generated social tags along with expert-generated LCSH descriptors in Sociology: a heuristic study

The study attempts to compare user-generated social tags with expert-generated LCSH descriptors of one thousand sociology books. The objective is to examine if social tags can be used to enhance the accessibility of library collections. The study found that both datasets do not follow the same vocabulary. Though, the Spearmans’ rank correlation (0.89) indicates a good association between common terms in both vocabularies. The Jaccard similarity coefficient (J = 0.13, 0.14, 0.17, 0.15 and 0.16) in different word clusters proves that top frequent social tags and top frequent LCSH descriptors used by users and experts are different. The comparison with each book also reveals that 555 books (55.5%) have 50 to 100 percent matching between both vocabularies. LCSH descriptor vocabulary contains more subject terms (24) than social tag vocabulary (12) out of the top thirty frequent terms. The comparison of social tags with MARC subfields ($a, $x, $y, $z, $v) reveals that users use more or less all the subfield terms as tags but either they do not use chronological terms ($y) for tags or use different terms other than experts for chronological information. Further, comparison with each book title reveals that social tags alongside LCSH descriptors can enhance the title-based search of libraries. Moreover, the study suggests that usage of social tags will not only enhance the accessibilities of library resources under sociology but also complement to controlled vocabularies by supplementing a variety of terms other than experts.

Evolution of data science and its education in iSchools: An impressionistic study using curriculum analysis

AbstractData Science (DS) has emerged from the shadows of its parents—statistics and computer science—into an independent field since its origin nearly six decades ago. Its evolution and education have taken many sharp turns. We present an impressionistic study of the evolution of DS anchored to Kuhn's four stages of paradigm shifts. First, we construct the landscape of DS based on curriculum analysis of the 32 iSchools across the world offering graduate‐level DS programs. Second, we paint the “field” as it emerges from the word frequency patterns, ranking, and clustering of course titles based on text mining. Third, we map the curriculum to the landscape of DS and project the same onto the Edison Data Science Framework (2017) and ACM Data Science Knowledge Areas (2021). Our study shows that the DS programs of iSchools align well with the field and correspond to the Knowledge Areas and skillsets. iSchool's DS curriculums exhibit a bias toward “data visualization” along with machine learning, data mining, natural language processing, and artificial intelligence; go light on statistics; slanted toward ontologies and health informatics; and surprisingly minimal thrust toward eScience/research data management, which we believe would add a distinctive iSchool flavor to the DS.

Augmented latent Dirichlet allocation model via word embedded clusters for mashup service clustering

AbstractAlong with the growing number of mashups, there have been many changes in their diversity as well. With the creation of a wide variety of mashup services, it has become difficult to select and recommend appropriate web‐based APIs or services to launch an application based on the principle of mashup technology. In keeping with the methods already in place, we realized that the input/output parameters and text description which are key features directly from WSDL specification documents are increasingly being used in the mashup service innovation process. The quality of these methods can be enhanced by capturing hidden topic content from WSDL documents through the LDA (latent Dirichlet allocation). In this paper, the performance as concerns the LDA model can be enhanced by using some auxiliary features rather than limiting it to some extent. The reason for this is that the word vectors achieved by the word2vec tool is of advanced quality than the word vectors of the word gained by the LDA model. So, we proposed to innovate the novel mashup service innovation and clustering method with better efficiency and higher accuracy through word2vec. Here developed an improved LDA model with a word embedded cluster (called AUG‐LDA) that has a major impact on word vector quality. In this proposed approach, the LDA's training process can be semimonitored by first acquiring the word vectors through the word2vec tool and then merging the word clusters with the K‐means++ algorithm. This will enable better representations for the distribution of mashup services. This can be obtained by crawling the dataset from the website calledwww.programmableweb.comto perform a comprehensive experiment and demonstrate the proposed method. It has been proven that there is a better increase by comparing our approach with different metrics and the clustering accuracy values obtained by other methods.

Human’s Plexus Systems and “Nikola Tesla’s 369 Theory” for Forming Universe and God

All activities which are taking place in the Cosmos also exist in a human body in subtle micro-scale. Plexuses centers in a human body are the most mysterious kinds of energies. The six-center plexus system is the path of the Kundalini shakti, the primordial cosmic energy of a person. Each plexus has its own propensities (vibrating words/dimensions/vritti) and an acoustic root. These plexuses control some cluster of words of sounds and corresponding physical organs in human body. The 50 main propensities of the human’s unit mind are expressed through the vibration-expression of these plexuses. These vibrations cause hormones to secrete from the corresponding glands of the human’s body. These plexuses can play very important roles in curing the diseases. According to the Theory of Nikola Tesla’s 369, there are all together 1 to 9 digital root numbers exist. All other higher or lower numbers are the combination of those digital root numbers. This statement seems quite true when we compare it with Theory of Absolutivity. If 3, 6, 9 numbers are arranged together it makes a letter Ohm (AUM) which represents actually a Sagun Bramh/Nikola Tesla’s 369 triangle which is a part of NirgunBramh having higher dimensions. Number zero is the NirgunBramh (having all the qualities but in dormant form) which does not possess any quality in action. Number 3, 6, 9 forming as an equilateral triangle of static, mutative and sentient forces representing the Black-hole which is a part of Sagun Bramh projecting Himself as a Visible World through Big-bang along with forming five basic elements such as Ethereal, Areal, Fire, Liquid and Solid. According to the biological science, life emerged in this planet from matter about four billion years ago in the form of DNA that carried the software for the development of billion of species on the way to its final destination in humans. These facts also fully support the above hypothesis of universe formations.

Text Analysis of Suicide Risk in Adolescents and Young Adults.

Suicide is of primary public concern for adolescents and young adults (AYAs) who commonly use social media platforms to express their suicidal thoughts and behaviors. Understanding how AYAs communicate their suicide-related thoughts and behaviors in texts can support early detection of suicide risk from their social media posts. Therefore, this study sought to identify themes relevant to suicide risk in AYAs and explore words or terms used by AYAs when they described suicidal thoughts and behaviors. This secondary data analysis utilized an existing data set collected from 255 AYAs between 12 and 25 years of age, who provided brief descriptions of how they and their peers expressed their experiences of self-harm, suicidal thinking, and attempts. Text analysis was conducted using KH Coder software. Three-step theory of suicide was used to guide a content analysis to explore the key themes from the narratives. A word co-occurrence network with 24 clusters of words was generated from the text analysis. These word clusters were further grouped into pain or hopelessness, connectedness, and capacity to attempt suicide in the content analysis. Six subthemes corresponding to these three themes were identified to provide detailed information: psychological or physical pain, hopelessness, relationship, help seeking, methods, and outcomes. Moreover, several slang terms and acronyms (e.g., Kermit Sewage Slide, KMS) were also identified. The findings of this study, including themes and slang terms and acronyms, are valuable to facilitate the use of terms or phrases within social media texts to identify suicide risk in AYAs.

Intact fluency in autism? A comprehensive approach of verbal fluency task including word imageability and concreteness.

Verbal fluency is a cognitive function reflecting executive functions and the ability to retrieve the appropriate information from memory quickly. Previous studies reported conflicting results—impaired and intact verbal fluency—in autism spectrum disorder (ASD). Most studies concentrate on overall word productivity, errors, perseverations, clustering, or switching. We used a comprehensive approach to evaluate the reported discrepancy in the literature and introduced a new angle using the concept of word abstraction and imageability. Moreover, we analyzed the performance in two‐time intervals (0–30 s and 31–60 s) to assess the temporal dynamics of verbal fluency and a possible activation or initiation deficit in autism. Sixteen adults with ASD and 16 neurotypical control participants, matched by gender, age, and education level, participated in our study. Contrary to our expectations, we did not find a significant difference between groups in word productivity, the number of errors, clustering, or temporal dynamics, neither in semantic nor in phonemic fluency tasks. Surprisingly, the two study groups' performance did not differ in terms of imageability or concreteness characteristics either. Our results raise the possibility that verbal fluency performance is intact in autism. We also suggest using a comprehensive approach when measuring fluency in autism.Lay summaryPeople with autism tend to think and communicate differently. In our study, we tested whether people with autism come up with more concrete or imageable words and whether their performance is better compared with neurotypicals in the beginning or in the later phase of a task measuring how many words they can produce in a minute. We did not detect any difference between the two groups; however, we recommend studying verbal fluency in autism from more and different angles in the future.

Research Trends and Hotspots of Q Fever Research: A Bibliometric Analysis 1990-2019.

Q fever is a worldwide distributed zoonosis caused by Coxiella burnetii, a Gram-negative bacterium. Despite existence of large amount of research data on the developments related to Q fever, no bibliometric analysis of this subject is available to our knowledge. Bibliometric studies are an essential resource to track scholarly trends and research output in a subject. This study is aimed at reporting a bibliometric analysis of publications related to Q fever (2,840 articles published in the period 1990-2019) retrieved from Science Citation Index Expanded, an online database of Clarivate Analytics Web of Science Core Collection. Data was retrieved using keywords “Q fever” or “Coxiella burnetii” in title, abstract, and author keywords to describe important research indicators such as the kind and language of articles, the most important publications, research journals and categories, authors, institutions, and the countries having the most significant contribution to this subject. Finally, the emerging areas in field of diagnosis, host range, and clinical presentation were identified. Word cluster analysis of research related to Q fever revealed that major focus of research has been on zoonosis, seroprevalence, laboratory diagnosis (mainly using ELISA and PCR), clinical manifestations (abortion and endocarditis), vectors (ticks), and hosts (sheep, goat, and cattle). This bibliometric study is intended to visualize the existing research landscape and future trends in Q fever to assist in future knowledge exchange and research collaborations.

Using bibliometric analysis to map innovative business models for vertical farm entrepreneurs

PurposeThis paper aims to explore the literature on vertical farming to define key elements to outline a business model for entrepreneurs. The research aims to stimulate entrepreneurship for vertical farming in a smart cities' context, recognising urban agriculture as technology to satisfy increasing food needs.Design/methodology/approachThe research conducts a structured literature review on 186 articles on vertical farming extracted from the Scopus. Moreover, the bibliometric analysis revealed the descriptive statistics on this field and the main themes through the authors' keywords.FindingsDifferent perspectives showed the multidisciplinary nature of the topic and how the intersection of different skills is necessary to understand the subject entirely. The keywords analysis allowed for identifying the topics covered by the authors and the business model's elements.Research limitations/implicationsThe research explores a topic in the embryonic stage to define key strands of literature. It provides business model insights extending George and Bock's (2011) research to stimulate entrepreneurship in vertical farming. Limitations arise from the sources used to develop our analysis and how the topic appears as a frontier innovation.Originality/valueOriginality is the integration of literature strands related to vertical farming, highlighting its multidisciplinary nature to provide a holistic understanding of the themes. In smart cities' context, innovations allow traditional business models to be interpreted in a novel perspective and revealed the elements for transforming vertical farming from innovative technology to an effective source of food sustenance. Finally, the paper suggests a new methodology application for the analysis of word clusters by integrating correspondence analysis and multidimensional scaling analysis.

A Simple and Effective Usage of Word Clusters for CBOW Model

We propose a simple and effective method for incorporating word clusters into the Continuous Bag-of-Words (CBOW) model. Specifically, we propose to replace infrequent input and output words in CBOW model with their clusters. The resulting cluster-incorporated CBOW model produces embeddings of frequent words and a small amount of cluster embeddings, which will be fine-tuned in downstream tasks. We empirically show our replacing method works well on several downstream tasks. Through our analysis, we show that our method might be also useful for other similar models which produce word embeddings.

Thai Named Entity Recognition Using BiLSTM-CNN-CRF Enhanced by TCC

The languages spoken in Asia share common morphological analysis errors in word segmentation which normally propagate to higher-level processing, i.e., POS tagging, syntactic parsing, word extraction, and NER, as we discuss in this research. We introduce the Thai character cluster (TCC) to reduce the errors propagated from word segmentation and POS tagging by incorporating into character representation layer of BiLSTM for NER. The initial NER model is created from the original THAI-NEST named-entity tagged corpus by applying the best performing BiLSTM-CNN-CRF model with word, part-of-speech, and character cluster embedding. We determine the errors and improve the consistency of the NE annotation through our holdout method by retraining the model with the corrected training set. After the iteration, the overall result of annotation F1-score has been improved to reach 89.22%, which improves 16.21% from the model trained on the original corpus. The result of our iterative verification is a promising method for low resource language modeling. As a result, The NE silver standard corpus is newly generated for the Thai NER task, called BKD Corpus (Bangkok Data NE tagged Corpus). The consistency of annotation is checked and revised according to the improvement of the scope of NE detection by TCC which can recover the errors in word segmentation.

ANALOGIES BETWEEN IMAGES AND TEXTS: 'CLUSTERIZATION' PHENOMENON IN TEXTS AND DIGITAL IMAGES

We develop a number of analogies between texts and images, where different pixel-brightness levels (or gray values) in an image correspond to different linguistic elements of some level (such as letters, symbols, words, etc.) in a text. This correspondence is possible only in the case of discrete structures such as digital images. A number of practical recipes for converting two-dimensional images into one-dimensional linear chains of pixels (i.e., ‘texts’) are discussed. In particular, this can be consideration of separate rows (or columns) in an image – or a series of sequential rows (or columns) in it, where a sequential number of pixel in a ‘one-dimensional image’ is analogue of a position of linguistic element in a text. The advantages and shortcomings of these recipes are analyzed. Besides, we introduce analogues of the statistical-linguistic notions of ‘vocabulary’ and ‘rank dependence’ in the case of images. Then we clarify the possibilities for application to digital images of standard statistical-linguistic techniques for detecting keywords in texts. In particular, we remind of the phenomenon of clustering of words in a text, which is very weak or absent for so-called function words – and pronounced for keywords. A simplest clustering parameter R is introduced that relates to the first statistical moments of the probability distribution for the waiting times of a given word in a text. It quantizes the scale of the above phenomenon for this word. After that, we analyze the latter phenomenon in the ‘texts’ that correspond to two different digital images. Our main result is that the ‘texts’ corresponding to a pure white noise and a simple informative image differ notably by their R -parameters associated with different ‘words’ (i.e., brightness levels). A possible meaning of ‘keywords’ in an image is discussed, which can be associated with some ‘semantic load’ of the appropriate brightness levels. We also advise to check the availability of long-range correlations among the same brightness levels in an image. This can be done according to one of standard techniques known for the studies of correlations (e.g., fluctuation analysis or detrended fluctuation analysis). Key words : statistical linguistics, natural language processing, clustering, keywords, digital image processing.

Inducing Comprehension and Emotions Through Discourse Multimodality: The Use of Language, Image and Sound

This article aims to highlight the multimodal features which underlie the architecture of meaning, emphasizing that the use of images and sounds in the construction of the text is a powerful tool to help the reader to make sense of any text genre. It is a well-known fact that communication is a complex process due to the fact that it comprises many steps. Since any text is just a proposal for the construction of meaning, the recipient’s role is very active, continually pairing their long-term memory with the coded message to cognitively reconstruct the meaning accordingly. Therefore, we claim that since the spoken language has a strong neural basis, which dates back to the first months of life, and the written language is intrinsically related to the visual system, the use of figures of speech (images) and figures of sound (syntactic stylistic devices) trigger both the right and left hemispheres of the brain and cause them to work in harmony, inducing comprehension and emotion. In this sense, the classic style, based on functional and cognitive principles of information structure, is highly recommended for a more generic readership of any kind of text genre, not only literary but also non-literary. In fact, some authors have recently argued that it seems to be easier for readers to make sense of texts when they evoke images and the feeling of sound, a phenomenon called phonesthetics, in which the aesthetic features of individual sounds and sound clusters of words and even structures reverberate in our minds, enhancing the understanding of an idea.

A Corpus-Driven Study of the Style Variation in The Grapes of Wrath

The novel The Grapes of Wrath is distinctive in the arrangement of intercalary chapters and narrative chapters. Existing studies of the narratological distinction of this novel are primarily qualitative. This article conducted a corpus-driven study of the variation of styles in this novel from the perspectives of word cluster, type-token ratio, descriptivity and activity, keyness, and sentiment. The cluster analysis shows that the choice of words in the narrative chapters is more consistent than that in the intercalary chapters. The type-token ratio analysis testifies to the heterogeneity of the intercalary chapters in terms of lexical richness. The descriptivity and activity analysis and the keyness analysis reveal that the narrative chapters are more active than the intercalary chapters. The sentiment analysis finds that the novel is pervaded by negative sentiments and that negative sentiments are more prevalent in the narrative chapters than in the intercalary chapters. The research concludes that the corpus-driven study can provide insights into the narrative structure and the stylistic variation of the novel.

Challenges in managing semantic annotations in harvested research objects in a national CRIS context

Harvested metadata on research objects can include links between the primary domain objects such as organizational identifiers associated with dataset, persons identified with ORCIDs linked to publications and publications connected through ISSNs to publishing channels. This kind of linkage is the bread-and-butter of the CRIS systems and usually comprehensively maintained. When it comes to the more subjective description of a domain object, such as keywords, themes, or subject headings, the issues related to data management and modeling become prominent with challenges such as flexibility of free text keywords as opposed to authoritative, but rigid classification systems. Many CRIS objects also already contain an extensive description of the content, just meant for human consumption, in the form of an abstract or similar summary text.With the help of automated data mining and annotation tools, these textual representations can be processed into structured data. This paper presents the processing pipelines implemented as part of the research.fi portal for automatic linking of different research inputs based on automatically extracted ontology concepts and discusses the implications of utilizing them as part of the research.fi platform. But more than simply discussing the annotation of research objects and the creation of word clusters for representation of the semantic content of research objects, we also discuss challenges related to maintaining the automatically produced metadata

Contested social representations of a religious ritual in the Philippines: Text mining online discourses on the Traslación

The article examines the production of contested social representations of a Philippine religious ritual in online spaces using a mixed methods approach to semantic network analysis. Comments from the online media coverage of the 2019 Traslación were mined from the Facebook pages of the eight most popular Philippine media outlets. A semantic network consisting of collocation of frequently used words was generated using R and Gephi. Network analysis produced three word clusters highlighting bifurcations linguistically, in the use of Filipino or English, and substantively, in contested representations. Analysis of these themes reveals the centrality of discourses reifying the ritual as authentic expressions of faith. A minority of discourses reflect criticisms of the Traslación as idolatry. This reflects the dichotomy of discourses in the Traslación literature. The research demonstrates the relevance of social computing in the analysis of meanings of cultural phenomena across large populations.

Classification symbols of words

The article looks at the classification features of word clusters, a complex and frequently discussed issue such as word classification in the history of linguistics. Indeed, the word as a spiritual and grammatical whole requires each researcher to determine its nature, lexical and grammatical description accordingly, as well as to study the classification of words and their division into lexical-grammatical groups for linguists of each period, remains an important issue. In a language system, all lexical units are required to have semantic, syntactic, and morphological features. Therefore, as the role and meaning of words in the system are determined, it is necessary to pay attention to the same three signs of them. In the classification of words, these signs are generalized. The possibilities of categorizing words prove once again that language is a holistic system. In the language system, the differentiation of events in a particular group is limited to a distinctive sign at a particular level, and there is no need to refer to other levels. For this reason, while categories such as noun, adjective, number, and verb differ morphologically, the similarity of semantic and syntactic characters is sought.

Global Research and Scientific Publications on Dexmedetomidine Use in Adults for Anesthesia Care between 2001 and 2021: A Bibliometric Analysis

BackgroundAn increasing number of studies on dexmedetomidine use in adults have been published, but the effectiveness of dexmedetomidine remains contentious. ObjectiveThis study aimed to describe the changing trends and structural relationships of scientific achievements regarding dexmedetomidine over the past 2 decades and provide researchers with information to help them explore better research opportunities. MethodsQuantitative data of publications were retrieved from the Scopus database. Analyses of co-occurrence and collaboration among authors, countries, and key words were conducted using VOSviewer 1.6.17 software. Weighted occurrence and average publications per year were calculated. ResultsThe 1868 publications retrieved showed an increasing trend of annual publications on dexmedetomidine use in adults between 2001 and 2021. China accounted for the largest contribution to publications in the world (n = 577 [30.89%]). Four key word clusters indicating research hotspots were identified using VOSviewer. The number of articles published in the top 10% of journals in the United States was the highest among all publications from the country (97 out of 201 [48.26%]). Journals from the United Kingdom obtained the highest CiteScore (16.56). Journal of Anaesthesiology Clinical Pharmacology published the highest number of articles on this topic (n = 56). Wang authored the highest number of published articles (n = 42). Recent publications focused on the theme of cytokines and immunomodulation. Sufentanil attracted particular interest as a drug-related key word. Moreover, meta-analysis is becoming an increasingly popular research method in this field. ConclusionsThe increasing number of publications on dexmedetomidine use reflects growing interest in this topic. Future research should focus on meta-analyses to identify the most effective therapeutic methods. The immunomodulatory effect of dexmedetomidine on health and disease is of particular interest.

Using Latent Dirichlet Allocation and Text Mining Techniques for Understanding Medical Literature

Over the past few years, numerous studies and research articles have been published in the medical literature review domain. The topics covered by these researches included medical information retrieval, disease statistics, drug analysis, and many other fields and application domains. In this paper, we employ various text mining and data analysis techniques in an attempt to discover trending topics and topic concordance in the healthcare literature and data mining field. This analysis focuses on healthcare literature and bibliometric data and word association rules applied to 1945 research articles that had been published between the years 2006 and 2019. Our aim in this context is to assist saving time and effort required for manually summarizing large-scale amounts of information in such a broad and multi-disciplinary domain. To carry out this task, we employ topic modeling techniques through the utilization of Latent Dirichlet Allocation (LDA), in addition to various document and word embedding and clustering approaches. Findings reveal that since 2010 the interest in the healthcare big data analysis has increased significantly, as demonstrated by the five most commonly used topics in this domain.

Potential Effect of Informal Learning of English Through Watching a Medical Television Series on the Development of Maritime Medical English Competence

As a result of the widespread use of online technologies and vast opportunities for the use of English in everyday online life, the field of online informal learning of languages, in particular English, has attracted a new wave of research attention. Nevertheless, the number of corpus studies in this field remains low. More specifically, to date no research study has focused on the suitability of the language input to which online users are exposed while performing online activities with regard to the development of language skills in English as a language for specific purposes. In order to bridge this research gap, the objective of this paper is to apply the corpus approach to examine whether watching a medical television series may have an effect on the development of medical Maritime English for future deck officers. The results indicate that in terms of lexical density, lexical diversity, terminology, and word clusters, watching a medical television series may have a limited effect on the development of medical Maritime English for future deck officers. However, future research should examine whether watching television series may have an effect on the acquisition of typical speech patterns in spoken maritime communication, which are closely related to everyday spoken communication. Importantly, the results also seem to indicate that online informal learning of English cannot replace all segments of the formal learning of English for Specific Purposes, but can significantly contribute to the development of general English competence, which is a precondition for the further development of discipline-specific language competence.