• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.834-839
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• 2007

The superimposed text in video brings important semantic clues into content analysis. In this paper, we present the new and fast superimposed text localization method in video segments. We detect the superimposed text by using temporal information contained in the video. To detect the superimposed text fast, we have minimized the candidate region of localizing superimposed texts by using the difference between consecutive frames. Experimental results are presented to demonstrate the good performance of the new superimposed text localization algorithm.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.515-521
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• 2007

The text categorization is an important field for the automatic text information processing. Moreover, the authorship identification of a text can be treated as a special text categorization. This paper adopts the conceptual primitives' expression based on the Hierarchical Network of Concepts (HNC) theory, which can describe the words meaning in hierarchical symbols, in order to avoid the sparse data shortcoming that is aroused by the natural language surface features in text categorization. The KNN algorithm is used as computing classification element. Then, the experiment has been done on the Chinese text authorship identification. The experiment result gives out that the processing mode that is put forward in this paper achieves high correct rate, so it is feasible for the text authorship identification.

• Educational Technology International
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• v.13 no.1
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• pp.49-77
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• 2012

The development of mobile devices and network technology is changing the ways in which people communicate with one another. Mobile text message has emerged as one of the most frequently used form of communication, which also gave rise to various non-verbal texts such as emoticons. Nonetheless, the use of text messages has largely been denied in education because text messages often involve colloquial and non-verbal texts considered inappropriate or grammatically incorrect by the teacher. In efforts to provide a theoretical framework to better understand mobile e-text communication, this research compared the practical usages of non-verbal texts in the mobile e-learning environment. The study developed three types of text messages according to the degree of using non-verbal texts and their phraseology as instructors' messages, which were then distributed to 259 students via mobile text messaging. The perceptions of students were analyzed using a semantic differential scale and a questionnaire. The results showed clear differences in students' perceptions of non-verbal text and traditional text, and that optimally designed non-verbal texts turned out to encourage the students' interaction the most out of the three types of text messages. Following the discussion of the results, an expanded theoretical framework beyond Ong's concepts of orality and literacy is also suggested to understand the evolution of mobile e-text communication in education.

• Journal of the Ergonomics Society of Korea
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• v.26 no.2
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• pp.149-155
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• 2007

Recently, various devices requiring text input such as mobile phone IPTV, PDA and UMPC are emerging. The frequency of text entry for them is also increasing. This study was focused on the evaluation of Korean text entry interface. Various models to evaluate text entry interfaces have been proposed. Most of models were based on human cognitive process for text input. The cognitive process was divided into two components; visual scanning process and finger movement process. The time spent for visual scanning process was modeled as Hick-Hyman law, while the time for finger movement was determined as Fitts' law. There are three questions on the model-based evaluation of text entry interface. Firstly, are human cognitive processes (visual scanning and finger movement) during the entry of text sequentially occurring as the models. Secondly, is it possible to predict real text input time by previous models. Thirdly, does the human cognitive process for text input vary according to users' text entry speed. There was time gap between the real measured text input time and predicted time. The time gap was larger in the case of participants with high speed to enter text. The reason was found out investigating Eye-Hand Coordination during text input process. Differently from an assumption that visual scan on the keyboard is followed by a finger movement, the experienced group performed both visual scanning and finger movement simultaneously. Arrival Lead Time was investigated to measure the extent of time overlapping between two processes. 'Arrival Lead Time' is the interval between the eye fixation on the target button and the button click. In addition to the arrival lead time, it was revealed that the experienced group uses the less number of fixations during text entry than the novice group. This result will contribute to the improvement of evaluation model for text entry interface.

• Smart Media Journal
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• v.1 no.4
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• pp.27-34
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• 2012

In this paper, a novel text binarization is presented that can deal with some complex conditions, such as shadows, non-uniform illumination due to highlight or object projection, and messy backgrounds. To locate the target text region, a focus line is assumed to pass through a text region. Next, connected component analysis and stroke width estimation based on location information of the focus line is used to locate the bounding box of the text region, and each box of connected components. A series of classifications are applied to identify whether each CC(Connected component) is text or non-text. Also, a modified K-means clustering method based on an HCL color space is applied to reduce the color dimension. A text binarization procedure based on location of text component and seed color pixel is then used to generate the final result.

• Journal of Korean Elementary Science Education
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• v.29 no.4
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• pp.526-537
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• 2010

The purpose of this study was to compare the effects of two different types of texts, which were narrative and expository, on the understanding of content. Elementary students' perceptions of the two types of the texts were also investigated. In the comparison of the effects on the understanding of the text contents, test scores of mind-mapping, closed-answer question, and essay test were used. The analyses of mind-mapping tests showed narrative text was more effective to figure out main concepts of the text throughout the mind-mapping test. But expository text was more effective in the hierarchical organization of the concepts. In the closed-answer questions and essay test, narrative text was more effective than expository text. However when the contents of text were difficult and complex, there was no meaningful difference between the two types of texts. The analyses of students' perceptions of the texts showed that narrative texts were preferred. Students perceived that the narrative text was more interesting and familiar. However, the perceptions of helpful text for their science learning were not different by the types of texts.

• Journal of The Korean Association For Science Education
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• v.30 no.1
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• pp.27-41
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• 2010

Scientific texts are some of major sources for scientific understanding. Therefore, reading scientific texts should be considered as an important learning activity. However, there is little research about reading scientific text in Korea. In this study, as a starting point for research about reading scientific text, lists of scientific text constituents and scientific text functions are suggested based on a comprehensive literature review. The study also reviewed how scientific text structure, familarity of scientific text and analogy involved in scientific text can affect students' scientific understanding through reading scientific text. Finally, further study plans, such as analysis of actual science textbooks using the lists suggested in this study as well as the investigation of actual students' thinking processes when reading scientific text, were described.

• Journal of the Korean Society of Physical Medicine
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• v.14 no.3
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• pp.55-62
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• 2019

PURPOSE: Text-mining has been shown to be useful for understanding the clinical characteristics and patients' concerns regarding a specific disease. Low back pain (LBP) is the most common disease in modern society and has a wide variety of causes and symptoms. On the other hand, it is difficult to understand the clinical characteristics and the needs as well as demands of patients with LBP because of the various clinical characteristics. This study examined online texts on LBP to determine of text-mining can help better understand general characteristics of LBP and its specific elements. METHODS: Online data from www.spine-health.com were used for text-mining. Keyword frequency analysis was performed first on the complete text of postings (full-text analysis). Only the sentences containing the highest frequency word, pain, were selected. Next, texts including the sentences were used to re-analyze the keyword frequency (pain-text analysis). RESULTS: Keyword frequency analysis showed that pain is of utmost concern. Full-text analysis was dominated by structural, pathological, and therapeutic words, whereas pain-text analysis was related mainly to the location and quality of the pain. CONCLUSION: The present study indicated that text-mining for a specific element (keyword) of a particular disease could enhance the understanding of the specific aspect of the disease. This suggests that a consideration of the text source is required when interpreting the results. Clinically, the present results suggest that clinicians pay more attention to the pain a patient is experiencing, and provide information based on medical knowledge.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.771-789
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• 2019

Text detection has been a popular research topic in the field of computer vision. It is difficult for prevalent text detection algorithms to avoid the dependence on datasets. To overcome this problem, we proposed a novel unsupervised text detection algorithm inspired by bootstrap learning. Firstly, the text candidate in a novel form of superpixel is proposed to improve the text recall rate by image segmentation. Secondly, we propose a unique text sample selection model (TSSM) to extract text samples from the current image and eliminate database dependency. Specifically, to improve the precision of samples, we combine maximally stable extremal regions (MSERs) and the saliency map to generate sample reference maps with a double threshold scheme. Finally, a multiple kernel boosting method is developed to generate a strong text classifier by combining multiple single kernel SVMs based on the samples selected from TSSM. Experimental results on standard datasets demonstrate that our text detection method is robust to complex backgrounds and multilingual text and shows stable performance on different standard datasets.