• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.1-7
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• 2024

Comparing text features involves evaluating the "similarity" between texts. It is crucial to use appropriate similarity measures when comparing similarities. This study utilized various techniques to assess the similarities between newspaper articles, including deep learning and a previously proposed method: a combination of Pointwise Mutual Information (PMI) and Word Pair Matching (WPM), denoted as PMI+WPM. For performance comparison, law data from medical research in Japan were utilized as validation data in evaluating the PMI+WPM method. The distribution of similarities in text data varies depending on the evaluation technique and genre, as revealed by the comparative analysis. For newspaper data, non-deep learning methods demonstrated better similarity evaluation accuracy than deep learning methods. Additionally, evaluating similarities in law data is more challenging than in newspaper articles. Despite deep learning being the prevalent method for evaluating textual similarities, this study demonstrates that non-deep learning methods can be effective regarding Japanese-based texts.

• Korean Journal of English Language and Linguistics
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• v.4 no.2
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• pp.117-149
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• 2004

The paper is to argue against the asymmetric CP-V2 analysis of Old English, according to which finite verbs invariably undergo movement into a clause-final T within subordinate clauses and reach the functional head C within main clauses. The asymmetric CP-V2 analysis, first of all, faces difficulty in explaining a wide range of post-verbal elements within subordinate clauses. To resolve the problem, the analysis has to abandon the obligatoriness of V-to-T movement or introduce various types of extraposition whose status is dubious as a legitimate syntactic operation. Obligatory V-to-T movement in Old English lacks conceptual justification as well. Crosslinguistic evidence reveals that morphological richness in verbal inflection cannot entail overt verb movement. Moreover, the operation is always string-vacuous under the asymmetric CP- V2 analysis and has no effect at the interfaces, in violation of the principle of economy. The distribution of Old English finite verbs in main clauses also undermines the asymmetric CP-V2 analysis. Conceptually speaking, a proper syntactic trigger cannot be confirmed to motivate obligatory verb movement to C. The operation not only gets little support from nominative Case marking, the distribution of expletives, or complementizer agreement but also requires the unconvincing stipulation that expletives as well as sentence-initial subjects result from string-vacuous topicalization. Finally, textual evidence testifies that Old English sometimes permits non-V2 ordering patterns, many of which remain unexplained under the asymmetric CP-V2 analysis.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.3
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• pp.544-559
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• 2023

Social discourses surrounding diversity, equity, and inclusion (DE&I) in the fashion industry are vital as they extend beyond language and encompass social practices. This study aimed to understand how discourses on DE&I with in the fashion industry are reconstructed and practiced in society. Therefore, this paper analyzed DE&I in the fashion industry, by focusing on the New York Times articles, employing a quantitative research model based on corpus analysis and a qualitative approach through critical discourse analysis. Results of the analysis of textual practice, showed that the New York Times emphasized black individuals as the central discourse and created a critical racial narrative regarding DE&I in the fashion industry characterized by a dichotomy of black vs. white confrontation. Furthermore, results of the discourse practice analysis revealed that the dichotomy of racial confrontation in the New York Times article tended to select the subject of discourse related to racial DE&I in the fashion industry according based on social and historical context. Thirdly, the analytical results of sociocultural practices indicated that the dichotomous racial discourse between black and white, propagated by the New York Times, spread across social media, transforming fashion from an industry to a domain where black individuals struggle for human rights.

• Women's Health Nursing
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• v.29 no.2
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• pp.128-136
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• 2023

Purpose: Topic modeling is a text mining technique that extracts concepts from textual data and uncovers semantic structures and potential knowledge frameworks within context. This study aimed to identify major keywords and network structures for each major topic to discern research trends in women's health nursing published in the Korean Journal of Women Health Nursing (KJWHN) using text network analysis and topic modeling. Methods: The study targeted papers with English abstracts among 373 articles published in KJWHN from January 2011 to December 2021. Text network analysis and topic modeling were employed, and the analysis consisted of five steps: (1) data collection, (2) word extraction and refinement, (3) extraction of keywords and creation of networks, (4) network centrality analysis and key topic selection, and (5) topic modeling. Results: Six major keywords, each corresponding to a topic, were extracted through topic modeling analysis: "gynecologic neoplasms," "menopausal health," "health behavior," "infertility," "women's health in transition," and "nursing education for women." Conclusion: The latent topics from the target studies primarily focused on the health of women across all age groups. Research related to women's health is evolving with changing times and warrants further progress in the future. Future research on women's health nursing should explore various topics that reflect changes in social trends, and research methods should be diversified accordingly.

• Journal of Korea Trade
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• v.24 no.5
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• pp.71-86
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• 2020

Purpose - This study investigates research trends by searching for English keywords and abstracts in 1,511 Korean journal articles in the Korea Citation Index from the 2002-2019 period using the term "Port." The study aims to lay the foundation for a more balanced development of port research. Design/methodology - Using abstract and keyword data, we perform frequency analysis and word embedding (Word2vec). A t-SNE plot shows the main keywords extracted using the TextRank algorithm. To analyze which words were used in what context in our two nine-year subperiods (2002-2010 and 2010-2019), we use Scattertext and scaled F-scores. Findings - First, during the 18-year study period, port research has developed through the convergence of diverse academic fields, covering 102 subject areas and 219 journals. Second, our frequency analysis of 4,431 keywords in 1,511 papers shows that the words "Port" (60 times), "Port Competitiveness" (33 times), and "Port Authority" (29 times), among others, are attractive to most researchers. Third, a word embedding analysis identifies the words highly correlated with the top eight keywords and visually shows four different subject clusters in a t-SNE plot. Fourth, we use Scattertext to compare words used in the two research sub-periods. Originality/value - This study is the first to apply abstract and keyword analysis and various text mining techniques to Korean journal articles in port research and thus has important implications. Further in-depth studies should collect a greater variety of textual data and analyze and compare port studies from different countries.

• Communications of Mathematical Education
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• v.32 no.3
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• pp.407-433
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• 2018

In order to analyze textbooks from a discursive approach, the purpose of this study is to structuralize an analytic framework based on previous literature review and apply it to analyzing the meanings and their syntheses developed by words and visual mediators appeared in the definition of graph in first-year middle school textbooks. The discursive approach consists of the communicational approach developed by Sfard(2008) and the systemic functional linguistics developed by Halliday(1985/2004). In this study, ideational meta-functions for ideational meanings and interpersonal meta-functions for interpersonal meanings were employed to analyze the meanings produced by words and visual mediators in textbooks, whereas textual meta-functions for textual meanings were used for analyzing the synthesized relationships between words and visual mediators. Results show that first, density in mathematical discourse was very high and subjects in mathematical activities were ambiguous in the ideational meanings of words, and behavior aspect was more emphasized than thinking aspect in the interpersonal meanings of words which request student participations. In the case of ideational meanings of visual mediators, there was a lack of narrative diagrams, whereas there were qualitative differences in the case of offer. Second, there was a need for promoting a wide range of diverse synthetic relationships between words and visual mediators for developing enriched mathematical meanings through the varying uses like specification, explanation, similarity, and complement. These results are so important that they provide a new analytic framework from a discursive approach to textbook analysis because not only words, but also visual mediators are analyzed as tools for producing meanings in mathematics textbooks and their synthetic relationships are also examined.

• English Language & Literature Teaching
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• v.17 no.3
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• pp.161-175
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• 2011

This paper examines Korean EFL high school students' reader responses on an expository text and a narrative text with the same topic. The purpose of the study is to investigate whether they have different reading models depending on the two genres and whether there are any differences depending on the learners' proficiency levels. The analysis focuses on textual, critical, and aesthetic reading models in the reader responses written in English by science-gifted high school students (N=30). The results show that the participants have different reading models in reading an expository text and a narrative text. They tend to read the expository text in a more critical way while reading the narrative text in a more personal and emotional way. Moreover, regardless of the proficiency levels, they wrote longer responses on the narrative text than the expository text. However, the proficiency level of English does not support any significant differences in the types of reading models. The findings provide Korean EFL high school students' characteristics in L2 reading and suggest the pedagogical implication to pursue linguistic development as well as reading for pleasure.

• Culinary science and hospitality research
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• v.22 no.1
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• pp.42-51
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• 2016

This study investigated the quality characteristics of sponge cake added with pine leaf powder. The pine leaf powder sponge cake was prepared with different ration of pine leaf powder(0, 10, 20, 30, 40%). The specific gravity, baking loss rate and cake weight increased significantly with increasing the levels of pine leaf powder. In terms of color, lightness and yellowness increased with increasing levels of pine leaf powder. The sponge cake added with ratio of 40% pine leaf powder appeared to be the highest. In terms of textual property evaluation, sponge cake were increased by the level of pine leaf powder. The substance's level of springiness, and cohesiveness decreased by increasing of the level of pine leaf powder. In sensory evaluation, 10% pine leaf e sponge cake was better on taste, overall acceptability, and flavor. The results showed that sponge cake quality with 10% pine leaf powder was considered the best.

• International Journal of Contents
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• v.14 no.1
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• pp.1-6
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• 2018

Text, as one of the most influential inventions of humanity, has played an important role in human life since ancient times. The rich and precise information embodied in text is very useful in a wide range of vision-based applications such as the text data extracted from images that can provide information for automatic annotation, indexing, language translation, and the assistance systems for impaired persons. Therefore, natural-scene text detection with active research topics regarding computer vision and document analysis is very important. Previous methods have poor performances due to numerous false-positive and true-negative regions. In this paper, a fully-convolutional-network (FCN)-based method that uses supervised architecture is used to localize textual regions. The model was trained directly using images wherein pixel values were used as inputs and binary ground truth was used as label. The method was evaluated using ICDAR-2013 dataset and proved to be comparable to other feature-based methods. It could expedite research on text detection using deep-learning based approach in the future.

• Journal of KIISE:Software and Applications
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• v.37 no.1
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• pp.54-59
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• 2010

Special-purpose microcontrollers PLCs have been widely used in the area of industrial automation. For the research of analysis and verification for PLC programs, first of all we have to specify formal sematics of PLC programming languages. This paper defines formally the operational semantics of LD language. After we transform the graphical language LD into its textual representation Symbolic LD, we give semantics of Symbolic LD since LD language is a graphical language. This paper defines the natural sematics of Symbolic LD and formalizes it in Coq proof assistant.