• English Language & Literature Teaching
• /
• no.3
• /
• pp.95-122
• /
• 1997

The purpose of this research was to investigate the effects of content and formal schema on reading comprehension. Five hundred fiftynine subjects from high school were assigned to one of the following levels and treatment conditions : (1) Higher level & Schema Activation, (2) Higher level & Non-schema Activation, (3) Lower level & Schema Activation, and (4) Lower level & Non-schema Activation. To evaluate the effects of schema activation. two experiments were conducted : one was related to the content schema and the other to the formal schema. To evaluate the effects of content schema, three different types of tests were conducted : (1) cloze test, (2) guessing the meanings of nonsense words, and (3) immediate recall test. To evaluate the effects of formal schema instruction, four kinds of tests were conducted : (1) sorting the sentences according to the importance, (2) identifying the signal words, (3) immediate recall test, and (4) identifying the specific information. For content schema condition, results indicated that the subjects given the titles or pictures before reading in "Content Schema Activation" treatment had better grades than those of the other treatment in all types of tests. regardless of their levels. Schema activation helped the subjects to increase the cognitive predictability of missing words and to participate in the tasks more actively with risk-taking. And it was also shown that good readers tend to process the words meaningfully, while poor readers tend to process the words phonetically or morphologically. Formal schema activation through teaching the text organization also had a significant influence on three types of tests: sorting the sentences according to the importance, identifying the signal words, and immediate recall test, but not on identifying the specific information. The implications from this study can be briefly noted as follows : (l) In teaching reading, the student's background knowledge should be activated as a pre-reading activity. (2) In reading, it is more important to emphasize the student's schema than the features of the text. (3) Various educational interventions should be introduced, especially for the lower level students. (4) Teaching text structures can be a powerful method for the top-down processing strategy.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.1
• /
• pp.401-407
• /
• 2015

The increase of image spam, a kind of spam in which the text message is embedded into attached image to defeat spam filtering technique, is a major problem of the current e-mail system. For nearly a decade, content based filtering using text classification or machine learning has been a major trend of anti-spam filtering system. Recently, spammers try to defeat anti-spam filter by many techniques. Text embedding into attached image is one of them. We proposed an ontology spam filters. However, the proposed system handles only text e-mail and the percentage of attached images is increasing sharply. The contribution of the paper is that we add image e-mail handling capability into the anti-spam filtering system keeping the advantages of the previous text based spam e-mail filtering system. Also, the proposed system gives a low false negative value, which means that user's valuable e-mail is rarely regarded as a spam e-mail.

• Journal of Information Processing Systems
• /
• v.10 no.1
• /
• pp.55-68
• /
• 2014

Automatic segmentation of foreground text from the background in degraded document images is very much essential for the smooth reading of the document content and recognition tasks by machine. In this paper, we present a novel approach to the binarization of degraded document images. The proposed method uses a new local contrast feature extracted based on the stroke width of text. First, a pre-processing method is carried out for noise removal. Text boundary detection is then performed on the image constructed from the contrast feature. Then local estimation follows to extract text from the background. Finally, a refinement procedure is applied to the binarized image as a post-processing step to improve the quality of the final results. Experiments and comparisons of extracting text from degraded handwriting and machine-printed document image against some well-known binarization algorithms demonstrate the effectiveness of the proposed method.

• International Journal of Advanced Culture Technology
• /
• v.9 no.1
• /
• pp.183-189
• /
• 2021

This study examines the effects of Korean college students' English proficiency and the English text types on their paraphrases. Korean college students with three groups of English proficiency (high, mid, and low) read two types of English texts, causal texts, and argumentative texts, and paraphrased them in English. Students' paraphrase text was evaluated in terms of content (idea exposition, idea development, and wrap up), organization (coherence and cohesion) and language use (grammatical accuracy), and analyzed by MANOVA. As a result, it was found that there was a significant difference in their paraphrase performance according to the participants' English proficiency levels rather than the types of English texts. The results of this study have educational implications for English paraphrase education to prevent plagiarism for Korean university students.

• Journal of Korea Multimedia Society
• /
• v.12 no.6
• /
• pp.867-875
• /
• 2009

More and richer information sources and services are available on the web everyday. However, harmful information, such as adult content, is not appropriate for all users, notably children. Since internet is a worldwide open network, it has a limit to regulate users providing harmful contents through each countrie's national laws or systems. Additionally it is not a desirable way of developing a certain system-specific classification technology for harmful contents, because internet users can contact with them in diverse ways, for example, porn sites, harmful spams, or peer-to-peer networks, etc. Therefore, it is being emphasized to research and develop context-based core technologies for classifying harmful contents. In this paper, we propose an efficient text filter for blocking harmful texts of web documents using context-based technologies and examine which algorithms for feature selection, the process that select content terms, as features, can be useful for text categorization in all content term occurs in documents, are suitable for classifying harmful contents through implementation and experiment.

• Human Ecology Research
• /
• v.59 no.2
• /
• pp.261-273
• /
• 2021

This study is not just a meaning of costume but a function of culture and includes addresses the associated emotions. As the interest of youths has increased recently, the importance of traditional costume education has been growing. Therefore, this study aims to analyze the contents of Hanbok in the 2015 revised high school technology and home textbooks using content analysis techniques and text mining techniques. As a result of the study, first, the symbolic meaning and characteristics of Hanbok and the beauty of Hanbok were practiced in daily life, and the value was found through the excellence of Hanbok and the modernization of Hanbok was dealt with Second, most of the illustrations related to traditional costumes were presented in various ways, but there were some regrets due to lack of quantity and quality. Third, the words used to explain traditional costumes were used in the form of culture, excellence, tradition, modernity, harmony, succession, etc. except for the types of clothing. Therefore, the results and discussions derived from this study are expected to help the textbooks to be efficiently selected and used in the field of the front line school along with the correct understanding of traditional culture in the process of selecting traditional culture contents and illustrations.

• Journal of the Korea Computer Industry Society
• /
• v.2 no.12
• /
• pp.1591-1600
• /
• 2001

There have been many WBI systems developed, but most of them do not fully consider instructional methods and learners' individual differences. If these are considered, a system will make a greater contribution to educational efficiency. We have developed a WBI prototype system that supports various instructional methods with varying instructional content. It presents instructional content that considers learners' individual differences. In this system, we provide interactive content based on multi-medium, interactive content based on text, and multi-medium presentation-stye content as one way teaching and text material appropriate to the learners' reference. Among the instructional methods, this system recommends a content appropriate to the individual learner. We provide individually proper feedback which considers the learner' misunderstanding in test modules. We tested our system in actual classes, evaluating and proofing our system for maximum educational effect.

• Journal of Internet Computing and Services
• /
• v.18 no.3
• /
• pp.49-61
• /
• 2017

Wikipedia infoboxes have emerged as an important structured information source on the web. To compose infobox for an article, considerable amount of manual effort is required from an author. Due to this manual involvement, infobox suffers from inconsistency, data heterogeneity, incompleteness, schema drift etc. Prior works attempted to solve those problems by generating infobox automatically based on the corresponding article text. However, there are many articles in Wikipedia that do not have enough text content to generate infobox. In this paper, we present an automated approach to generate infobox for movie domain of Wikipedia by extracting information from several sources of the web instead of relying on article text only. The proposed methodology has been developed using semantic relations of article content and available semi-structured information of the web. It processes the article text through some classification processes to identify the template from the large pool of template list. Finally, it extracts the information for the corresponding template attributes from web and thus generates infobox. Through a comprehensive experimental evaluation the proposed scheme was demonstrated as an effective and efficient approach to generate Wikipedia infobox.

• Journal of Internet Computing and Services
• /
• v.19 no.1
• /
• pp.155-166
• /
• 2018

These days, online media, such as blogospheres, online communities, and social networking sites, provides the uncountable user-generated content (UGC) to discover market intelligence and business insight with. The business has been interested in consumers, and constantly requires the approach to identify consumers' opinions and competitive advantage in the competing market. Analyzing consumers' opinion about oneself and rivals can help decision makers to gain in-depth and fine-grained understanding on the human and social behavioral dynamics underlying the competition. In order to accomplish the comparison study for rival products and companies, we attempted to do competitive analysis using text mining with online UGC for two popular and competing ramens, a market leader and a market follower, in the Korean instant noodle market. Furthermore, to overcome the lack of the Korean sentiment lexicon, we developed the domain specific sentiment dictionary of Korean texts. We gathered 19,386 pieces of blogs and forum messages, developed the Korean sentiment dictionary, and defined the taxonomy for categorization. In the context of our study, we employed sentiment analysis to present consumers' opinion and statistical analysis to demonstrate the differences between the competitors. Our results show that the sentiment portrayed by the text mining clearly differentiate the two rival noodles and convincingly confirm that one is a market leader and the other is a follower. In this regard, we expect this comparison can help business decision makers to understand rich in-depth competitive intelligence hidden in the social media.

• International Journal of Computer Science & Network Security
• /
• v.21 no.8
• /
• pp.87-96
• /
• 2021

The demand for e-learning through video lectures is rapidly increasing due to its diverse advantages over the traditional learning methods. This led to massive volumes of web-based lecture videos. Indexing and retrieval of a lecture video or a lecture video topic has thus proved to be an exceptionally challenging problem. Many techniques listed by literature were either visual or audio based, but not both. Since the effects of both the visual and audio components are equally important for the content-based indexing and retrieval, the current work is focused on both these components. A framework for automatic topic-based indexing and search depending on the innate content of the lecture videos is presented. The text from the slides is extracted using the proposed Merged Bounding Box (MBB) text detector. The audio component text extraction is done using Google Speech Recognition (GSR) technology. This hybrid approach generates the indexing keywords from the merged transcripts of both the video and audio component extractors. The search within the indexed documents is optimized based on the Naïve Bayes (NB) Classification and K-Means Clustering models. This optimized search retrieves results by searching only the relevant document cluster in the predefined categories and not the whole lecture video corpus. The work is carried out on the dataset generated by assigning categories to the lecture video transcripts gathered from e-learning portals. The performance of search is assessed based on the accuracy and time taken. Further the improved accuracy of the proposed indexing technique is compared with the accepted chain indexing technique.