• 한국IT서비스학회지
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• 제11권sup호
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• pp.139-152
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• 2012

In the medical web forum, people share medical experience and information as patients and patents' families. Some people search medical information written in non-expert language and some people offer words of comport to who are suffering from diseases. Medical web forums play a role of the informative support and the emotional support. We propose the automatic classification model of articles in the medical web forum into the information support and emotional support. We extract text features of articles in web forum using text mining techniques from the perspective of linguistics and then perform supervised learning to classify texts into the information support and the emotional support types. We adopt the Support Vector Machine (SVM), Naive-Bayesian, decision tree for automatic classification. We apply the proposed model to the HealthBoards forum, which is also one of the largest and most dynamic medical web forum.

• 한국임상약학회지
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• 제27권4호
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• pp.221-227
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• 2017

Background: As personalized healthcare industry has attracted much attention, big data analysis of healthcare data is essential. Lots of healthcare data such as product labeling, biomedical literature and social media data are unstructured, extracting meaningful information from the unstructured text data are becoming important. In particular, text mining for adverse drug reactions (ADRs) reports is able to provide signal information to predict and detect adverse drug reactions. There has been no study on text analysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. Methods: Expert opinion text of KAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the whole text, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis are performed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. Results: Among total 90,522 reports, 120 insulin ADR report and 858 tramadol ADR report were analyzed. The ADRs such as dizziness, headache, vomiting, dyspepsia, and shock were ranked in order in the insulin data, while the ADR symptoms such as vomiting, 어지러움, dizziness, dyspepsia and constipation were ranked in order in the tramadol data as the most frequently used keywords. Conclusion: Using text mining of the expert opinion in KIDS-KD, frequently mentioned ADRs and medications are easily recovered. Text mining in ADRs research is able to play an important role in detecting signal information and prediction of ADRs.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2004년도 가을 학술발표논문집 Vol.31 No.2 (1)
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• pp.799-801
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• 2004

인터넷 정보의 축적량이 증가함으로 인해 사용자는 원하는 정보를 찾기가 더욱 어려워졌다 따라서 수많은 문서들 중에서 원하는 정보를 효과적으로 찾아주는 정보검색 시스템의 중요성이 증가하게 되었으며 이에 대한 연구도 활발히 진행되었다. 인터넷 문서에서 추출할 수 있는 정보들은 링크 정보, Anchor Text 정보, Title Text 정보, 본문 Text 정보 등이 있으며, 이런 정보들을 이용한 수많은 정보검색 시스템이 개발되거나 모델이 연구되고 있다 본 논문에서는 기존에 이용되어 왔던 일반적인 추출 점보들을 정제 및 처리를 통해 성능을 높일 수 있는 방안을 연구했던 선행 연구를 기반으로 한 실험 결과 및 사이트 가중치를 추가한 모델을 제시한다.

• Communications for Statistical Applications and Methods
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• 제11권2호
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• pp.329-343
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• 2004

With advancement of computer and network, world wide web(WWW) as a medium of information communication is generalized in many fields. In educational aspect, applications of WWW as alternative media for class teachings or printed matters are increasing. In this article, we demonstrate a web-based electronic text on the 'probability and statistics' which is one of six fields of mathematics in the 7th curriculum. This text places importance on comprehension of concepts of probability and statistics as an applied science.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.41-46
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• 2023

Because of the significant role that harakat plays in Arabic text, this paper used deep learning to extract Arabic text with its harakat from an image. Convolutional neural networks and recurrent neural network algorithms were applied to the dataset, which contained 110 images, each representing one word. The results showed the ability to extract some letters with harakat.

• Journal of Information Processing Systems
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• 제5권1호
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• pp.5-10
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• 2009

Text data has always accounted for a major portion of the world's information. As the volume of information increases exponentially, the portion of text data also increases significantly. Text classification is therefore still an important area of research. LDA is an updated, probabilistic model which has been used in many applications in many other fields. As regards text data, LDA also has many applications, which has been applied various enhancements. However, it seems that no applications take care of the input for LDA. In this paper, we suggest a way to map the input space to a reduced space, which may avoid the unreliability, ambiguity and redundancy of individual terms as descriptors. The purpose of this paper is to show that LDA can be perfectly performed in a "clean and clear" space. Experiments are conducted on 20 News Groups data sets. The results show that the proposed method can boost the classification results when the appropriate choice of rank of the reduced space is determined.

• 한국안전학회지
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• 제33권6호
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• pp.77-84
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• 2018

Report documents of industrial and occupational accidents have continuously been accumulated in private and public institutes. Amongst others, information on narrative-texts of accidents such as accident processes and risk factors contained in disaster report documents is gaining the useful value for accident analysis. Despite this increasingly potential value of analysis of text information, scientific and algorithmic text analytics for safety management has not been carried out yet. Thus, this study aims to develop data processing and visualization techniques that provide a systematic and structural view of text information contained in a disaster report document so that safety managers can effectively analyze accident risk factors. To this end, the risk factor map using text mining and self-organizing map is developed. Text mining is firstly used to extract risk keywords from disaster report documents and then, the Self-Organizing Map (SOM) algorithm is conducted to visualize the risk factor map based on the similarity of disaster report documents. As a result, it is expected that fruitful text information buried in a myriad of disaster report documents is analyzed, providing risk factors to safety managers.

• 정보관리연구
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• 제36권4호
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• pp.51-69
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• 2005

문서분류에 있어서 분류속도의 향상이 중요한 연구과제가 되고 있다. 최근 개발된 자질값투표 기법은 문서자동분류 문제에 대해서 매우 빠른 속도를 가졌지만, 분류정확도는 만족스럽지 못하다. 이 논문에서는 새로운 자질선정 기법인 문서측 자질선정 기법을 제안하고, 이를 자질값투표 기법에 적용해 보았다. 문서측 자질선정은 일반적인 분류자질선정과 달리 학습집단이 아닌 분류대상 문서의 자질 중 일부만을 선택하여 분류에 이용하는 방식이다. 문서측 자질선정을 적용한 실험에서는, 간단하고 빠른 자질값투표 분류기로 SVM 분류기만큼 좋은 성능을 얻을 수 있었다.

• 영어어문교육
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• 제15권2호
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• pp.185-207
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• 2009

This study investigated the extent of informational text genre appeared in English textbooks at grades six, seven, and nine. Employing content analysis to analyze the literary forms, the researcher identified genre in each reading selection of each English textbook and classified it into six categories - fiction, information, biography, poetry, play, or fantasy. Especially, informational genre was classified further into two subcategories - non-narrative and narrative - in order to investigate the extent of non-narrative informational text only. The text genre was examined by analyzing (a) the number of reading selections representing each genre and (b) the number of words in reading selections devoted to each genre. The most frequent type of genre at grade 6 and 7 was fiction with 94% and 71% respectively, whereas at grade 9 it was devoted to information (51%), followed by fiction (37%). The largest number of words was devoted to fiction with 96% at the sixth grade and 70% at the seventh grade; on the other hand, for grade 9, it was devoted to information (46%), followed by fiction (39%). Although there was variance across different publishers, the informational text genre gained more significance as the grade level increased. In particular, the percentage of reading selections and words devoted to the non-narrative or expository informational genre was overall 4% at grade 6, 17% at grade 7, and 44% at grade 9. The findings demonstrated the need to pay more attention to informational literacy especially in the early grades for the development of balanced genre knowledge.

• 로봇학회논문지
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• 제16권3호
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• pp.276-282
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• 2021

The system for recognizing text in natural scenes has been applied in various industries. However, due to the change in brightness that occurs in nature such as light reflection and shadow, the text recognition performance significantly decreases. To solve this problem, we propose an adversarial shadow generation and training algorithm that is robust to shadow changes. The adversarial shadow generation and training algorithm divides the entire image into a total of 9 grids, and adjusts the brightness with 4 trainable parameters for each grid. Finally, training is conducted in a adversarial relationship between the text recognition model and the shaded image generator. As the training progresses, more and more difficult shaded grid combinations occur. When training with this curriculum-learning attitude, we not only showed a performance improvement of more than 3% in the ICDAR2015 public benchmark dataset, but also confirmed that the performance improved when applied to our's android application text recognition dataset.