• Annual Conference of KIPS
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• 2019.05a
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• pp.447-450
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• 2019

본 논문은 한국어 문장의 감성 분류를 위해 문장의 형태소, 음절, 자소를 입력으로 하는 합성곱층과 DenseNet 을 적용한 Text Multi-channel DenseNet 모델을 제안한다. 맞춤법 오류, 음소나 음절의 축약과 탈락, 은어나 비속어의 남용, 의태어 사용 등 문법적 규칙에 어긋나는 다양한 표현으로 인해 단어 기반 CNN 으로 추출 할 수 없는 특징들을 음절이나 자소에서 추출 할 수 있다. 한국어 감성분석에 형태소 기반 CNN 이 많이 쓰이고 있으나, 본 논문에서 제안한 Text Multi-channel DenseNet 모델은 형태소, 음절, 자소를 동시에 고려하고, DenseNet 에 정보를 밀집 전달하여 문장의 감성 분류의 정확도를 개선하였다. 네이버 영화 리뷰 데이터를 대상으로 실험한 결과 제안 모델은 85.96%의 정확도를 보여 Multi-channel CNN 에 비해 1.45% 더 정확하게 문장의 감성을 분류하였다.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.3
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• pp.427-439
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• 2019

This study investigated the semantic network based on the focus of the fashion image and SNS text utilized by global SPA brands on the last seven years in terms of the quantity and quality of data generated by the fast-changing fashion trends and fashion content-based media environment. The research method relocated frequency, density and repetitive key words as well as visualized algorithms using the UCINET 6.347 program and the overall classification of the text related to fashion images on social networks used by global SPA brands. The conclusions of the study are as follows. A common aspect of global SPA brands is that by looking at the basis of text extraction on SNS, exposure through image of products is considered important for sales. The following is a discriminatory aspect of global SPA brands. First, ZARA consistently exposes marketing using a variety of professions and nationalities to SNS. Second, UNIQLO's correlation exposes its collaboration promotion to SNS while steadily exposing basic items. Third, in the case of H&M, some discriminatory results were found with other brands in connectivity with each cluster category that showed remarkably independent results.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.211-213
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• 2022

Images in the textbooks influence the learning process. Students often see pictures before reading the text and these pictures can enhance the power of imagination of the students. The findings of some researches show that the images in textbooks can increase students' creativity. However, when learning major subjects, reading a textbook or looking at a picture alone may not be enough to understand the topics and completely realize the concepts. Studies show that viewers remember 95% of a message when watching a video than reading a text. If we can combine textbooks and videos, this teaching method is fantastic. The "TEXT + IMAGE + VIDEO (Animation)" concept could be more beneficial than ordinary ones. We tried to give our solution by using machine learning Image Classification. This paper covers the features, approaches and detailed objectives of our project. For now, we have developed the prototype of this project as a web app and it only works when accessed via smartphone. Once you have accessed the web app through your smartphone, the web app asks for access to use the camera. Suppose you bring your smartphone's camera closer to the picture in the textbook. It will then display the video related to the photo below.

• Journal of the Korean Chemical Society
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• v.67 no.5
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• pp.362-377
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• 2023

The purpose of this study was to investigate the effect of semantic mapping as a science text reading strategy on high school students' inferential understanding. For this purpose, eight science text reading classes were conducted a reading strategy using semantic mapping for 46 students in two science-focused classes in the third grade of a high school. To investigate the effects of semantic mapping reading strategy on students' inferential comprehension, students' pre- and post-reading ability tests results were analyzed. In order to find out the change in inferential comprehension, the level of the inferential comprehension was analyzed using the analysis framework for developed in this study. For the classification of inferential comprehension, the levels of the inferential comprehension were converted into scores. The results of the analysis of changes in students' inferential comprehension showed that semantic mapping reading strategy classes influenced the changes in high school students' inference, especially bridge inference and elaborative inference among sub-elements of inferential comprehension.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.1
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• pp.163-172
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• 2007

The web as global network includes text document, video, sound, etc and connects each distributed information using link Through development of web, it accumulates abundant information and the main is text based documents. Most of user use the web to retrieve information what they want. So, numerous researches have progressed to retrieve the text documents using the many methods, such as probability, statistics, vector similarity, Bayesian, and so on. These researches however, could not consider both the subject and the semantics of documents. As a result user have to find by their hand again. Especially, it is more hard to find the korean document because the researches of korean document classification is insufficient. So, to overcome the previous problems, we propose the korean document classification method for semantic retrieval. This method firstly, extracts TF value and RV value of concepts that is included in document, and maps into U-WIN that is korean vocabulary dictionary to select the topic of document. This method is possible to classify the document semantically and showed the efficiency through experiment.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.69-90
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• 2022

As basic data that can systematically support and evaluate R&D activities as well as set current and future research directions by grasping specific trends in domestic academic research, I sought efficient ways to assign standardized subject categories (control keywords) to individual journal papers. To this end, I conducted various experiments on major factors affecting the performance of automatic classification, focusing on feature selection techniques, for the purpose of automatically allocating the classification categories on the National Research Foundation of Korea's Academic Research Classification Scheme to domestic journal papers. As a result, the automatic classification of domestic journal papers, which are imbalanced datasets of the real environment, showed that a fairly good level of performance can be expected using more simple classifiers, feature selection techniques, and relatively small training sets.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.53-77
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• 2012

This study analyses the difference of contents and tones of arguments among three Korean major newspapers, the Kyunghyang Shinmoon, the HanKyoreh, and the Dong-A Ilbo. It is commonly accepted that newspapers in Korea explicitly deliver their own tone of arguments when they talk about some sensitive issues and topics. It could be controversial if readers of newspapers read the news without being aware of the type of tones of arguments because the contents and the tones of arguments can affect readers easily. Thus it is very desirable to have a new tool that can inform the readers of what tone of argument a newspaper has. This study presents the results of clustering and classification techniques as part of text mining analysis. We focus on six main subjects such as Culture, Politics, International, Editorial-opinion, Eco-business and National issues in newspapers, and attempt to identify differences and similarities among the newspapers. The basic unit of text mining analysis is a paragraph of news articles. This study uses a keyword-network analysis tool and visualizes relationships among keywords to make it easier to see the differences. Newspaper articles were gathered from KINDS, the Korean integrated news database system. KINDS preserves news articles of the Kyunghyang Shinmun, the HanKyoreh and the Dong-A Ilbo and these are open to the public. This study used these three Korean major newspapers from KINDS. About 3,030 articles from 2008 to 2012 were used. International, national issues and politics sections were gathered with some specific issues. The International section was collected with the keyword of 'Nuclear weapon of North Korea.' The National issues section was collected with the keyword of '4-major-river.' The Politics section was collected with the keyword of 'Tonghap-Jinbo Dang.' All of the articles from April 2012 to May 2012 of Eco-business, Culture and Editorial-opinion sections were also collected. All of the collected data were handled and edited into paragraphs. We got rid of stop-words using the Lucene Korean Module. We calculated keyword co-occurrence counts from the paired co-occurrence list of keywords in a paragraph. We made a co-occurrence matrix from the list. Once the co-occurrence matrix was built, we used the Cosine coefficient matrix as input for PFNet(Pathfinder Network). In order to analyze these three newspapers and find out the significant keywords in each paper, we analyzed the list of 10 highest frequency keywords and keyword-networks of 20 highest ranking frequency keywords to closely examine the relationships and show the detailed network map among keywords. We used NodeXL software to visualize the PFNet. After drawing all the networks, we compared the results with the classification results. Classification was firstly handled to identify how the tone of argument of a newspaper is different from others. Then, to analyze tones of arguments, all the paragraphs were divided into two types of tones, Positive tone and Negative tone. To identify and classify all of the tones of paragraphs and articles we had collected, supervised learning technique was used. The Na$\ddot{i}$ve Bayesian classifier algorithm provided in the MALLET package was used to classify all the paragraphs in articles. After classification, Precision, Recall and F-value were used to evaluate the results of classification. Based on the results of this study, three subjects such as Culture, Eco-business and Politics showed some differences in contents and tones of arguments among these three newspapers. In addition, for the National issues, tones of arguments on 4-major-rivers project were different from each other. It seems three newspapers have their own specific tone of argument in those sections. And keyword-networks showed different shapes with each other in the same period in the same section. It means that frequently appeared keywords in articles are different and their contents are comprised with different keywords. And the Positive-Negative classification showed the possibility of classifying newspapers' tones of arguments compared to others. These results indicate that the approach in this study is promising to be extended as a new tool to identify the different tones of arguments of newspapers.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.176-185
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• 1996

Transformin the huge size of drawings into a suitable format for CAD system and recognizng the contents of drawings are the major concerans in the automated analysis of engineering drawings. This paper proposes some methods for text/graphics separation, symbol extraction, vectorization and symbol recognition with the object of applying them to electronic cirucit drawings. We use MBR (Minimum bounding rectangle) and size of isolated region on the drawings for separating text and graphic regions. Characteristics parameters such as the number of pixels, the length of circular constant and the degree of round shape are used for extracting loop symbols and geometric structures for non-loop symbols. To recognize symbols, nearest netighbor between FD (foruier descriptor) of extractd symbols and these of classification reference symbols is used. Experimental results show that the proposed method can generate compact vector representation of extracted symbols and perform the scale change and rotation of extracted symbol using symbol vectorization. Also we achieve an efficient searching of circuit drawings.

• MALSORI
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• no.57
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• pp.165-174
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• 2006

In this paper we use a training algorithm, MCE (Minimum Classification Error), to improve the performance of a text-independent speaker identification system. The MCE training scheme takes account of possible competing speaker hypotheses and tries to reduce the probability of incorrect hypotheses. Experiments performed on a small set speaker identification task show that the discriminant training method using MCE can reduce identification errors by up to 54% over a baseline system trained using Bayesian adaptation to derive GMM (Gaussian Mixture Models) speaker models from a UBM (Universal Background Model).

• Journal of English Language & Literature
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• v.57 no.6
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• pp.911-938
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• 2011

The present study examined the effect of two variables of task complexity, reasoning demand and time pressure, each from the resourcedirecting and resource-dispersing dimension in Robinson's (2001) framework of task classification. Reasoning demand was operationalized as the two types of texts to read and summarize, expository and argumentative. Time pressure was operationalized as the two modes of performance, oral and written. Six university students summarized the two types of text orally and twenty four students from the same school summarized them in the written form. Results from t test and ANCOVA showed that in the oral mode, reasoning demand tends to heighten the complexity of the language used in the summary in competition with accuracy but such an effect disappeared in the written mode. It was interpreted that the degree of time pressure is not the only difference between the oral and written modes but that the two modes may be fundamentally different cognitive tasks, and that Robinson's (2001) and Skehan's (1998) models were differentially supported by the oral mode of tasks but not by the written mode of the tasks.