• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.185-207
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• 2022

Recently, with the development of deep learning technology, researches to apply a deep learning algorithm to analyze unstructured data such as text and images are being actively conducted. Text classification has been studied for a long time in academia and industry, and various attempts are being performed to utilize data characteristics to improve classification performance. In particular, a hierarchical relationship of labels has been utilized for hierarchical classification. However, the top-down approach mainly used for hierarchical classification has a limitation that misclassification at a higher level blocks the opportunity for correct classification at a lower level. Therefore, in this study, we propose a methodology for classifying hierarchical data using the autoencoder-based deeply supervised network that high-level classification does not block the low-level classification while considering the hierarchical relationship of labels. The proposed methodology adds a main classifier that predicts a low-level label to the autoencoder's latent variable and an auxiliary classifier that predicts a high-level label to the hidden layer of the autoencoder. As a result of experiments on 22,512 academic papers to evaluate the performance of the proposed methodology, it was confirmed that the proposed model showed superior classification accuracy and F1-score compared to the traditional supervised autoencoder and DNN model.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.4
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• pp.105-117
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• 2014

Acquisition of information through the broadcast media is essential for modern life and each broadcaster has progressed its service over the internet with the development of digital technology. Under this circumstance, this study presented the results which compared and analyzed the web accessibility evaluation for Korea, USA, and Japan's leading broadcaster web sites. According to the study results, there was no significant difference in the level of accessibility in all web sites of three countries, but accessibility compliance rate such as alternate text, skip-navigation of repeated region, and title was somewhat insufficient for Korean web sites. In addition, accessibility errors in the brightness contrast of the text contents, the run of the functions that a user doesn't have any intention, the clear statement of the default language, and the label provision were investigated. Therefore, Korean broadcasters should urgently improve and modify these errors and problems for effective web accessibility.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.25-31
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• 2018

Online news has replaced the traditional newspaper and has brought about a profound transformation in the way we access and share information. News websites have had the ability for users to post comments for quite some time, and some have also begun to crowdsource reactions to news articles. The field of sentiment analysis seeks to computationally model the emotions and reactions experienced when presented with text. In this work, we analyze more than 100,000 news articles over ten categories with five user-generated emotional annotations to determine whether or not these reactions have a mathematical correlation to the news body text and propose a simple sentiment analysis algorithm that requires minimal preprocessing and no machine learning. We show that it is effective even for a morphologically complex language like Korean.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.1
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• pp.415-439
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• 2014

The purpose of this study is to analyze mobile web accessibility of public libraries. To achieve this, accessibility checklists were driven from mobile web and application guidelines. The study also conducted automatic and manual evaluations. As results, the study found that text alternatives for resource image, main menu, image for text contents were mainly missing. In addition, focus for retrieval, operating system accessibility for retrieval, and id & password label, color difference for library calendar and seat reservation, brightness difference for main manu and resource display, and space between controls for retrieval were mainly missing. The study also suggested focal missing areas for main library services. It will be utilized as checklists for development of mobile services.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.147-163
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• 2016

Gender information of a smart device user is essential to provide personalized services, and multi-modal data obtained from the device is useful for predicting the gender of the user. However, the method for utilizing each of the multi-modal data for gender prediction differs according to the characteristics of the data. Therefore, in this study, an ensemble method for predicting the gender of a smart device user by using three classifiers that have text, application, and acceleration data as inputs, respectively, is proposed. To alleviate privacy issues that occur when text data generated in a smart device are sent outside, a classification method which scans smart device text data only on the device and classifies the gender of the user by matching text data with predefined sets of word. An application based classifier assigns gender labels to executed applications and predicts gender of the user by comparing the label ratio. Acceleration data is used with Support Vector Machine to classify user gender. The proposed method was evaluated by using the actual smart device log data collected from an Android application. The experimental results showed that the proposed method outperformed the compared methods.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.27-40
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• 2023

With the increase in the spread of smart devices and the impact of COVID-19, the consumption of media contents through smart devices has significantly increased. Along with this trend, the amount of media contents viewed through OTT platforms is increasing, that makes contents recommendations on these platforms more important. Previous contents-based recommendation researches have mostly utilized metadata that describes the characteristics of the contents, with a shortage of researches that utilize the contents' own descriptive metadata. In this paper, various text data including titles and synopses that describe the contents were used to recommend similar contents. KLUE-RoBERTa-large, a Korean language model with excellent performance, was used to train the model on the text data. A dataset of over 20,000 contents metadata including titles, synopses, composite genres, directors, actors, and hash tags information was used as training data. To enter the various text features into the language model, the features were concatenated using special tokens that indicate each feature. The test set was designed to promote the relative and objective nature of the model's similarity classification ability by using the three contents comparison method and applying multiple inspections to label the test set. Genres classification and hash tag classification prediction tasks were used to fine-tune the embeddings for the contents meta text data. As a result, the hash tag classification model showed an accuracy of over 90% based on the similarity test set, which was more than 9% better than the baseline language model. Through hash tag classification training, it was found that the language model's ability to classify similar contents was improved, which demonstrated the value of using a language model for the contents-based filtering.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.91-94
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• 2018

감정분석은 텍스트에서 나타난 저자 혹은 발화자의 태도, 의견 등과 같은 주관적인 정보를 추출하는 기술이며, 여론 분석, 시장 동향 분석 등 다양한 분야에 두루 사용된다. 감정분석 방법은 사전 기반 방법, 기계학습 기반 방법 등이 있다. 본 논문은 사전 기반 감정분석에 필요한 한국어 감정사전 자동 구축 방법을 제안한다. 본 논문은 영어 감정사전으로부터 한국어 감정사전을 자동으로 구축하는 방법이며, 크게 세 단계로 구성된다. 첫 번째는 영한 병렬말뭉치를 이용한 영한사전을 구축하는 단계이고, 두 번째는 영한사전을 통한 이중언어 그래프를 생성하는 단계이며, 세 번째는 영어 단어의 감정값을 한국어 단어의 감정값으로 전파하는 단계이다. 본 논문에서는 제안된 방법의 유효성을 보이기 위해 사전 기반 한국어 감정분석 시스템을 구축하여 평가하였으며, 그 결과 제안된 방법이 합리적인 방법임을 확인할 수 있었으며 향후 연구를 통해 개선한다면 질 좋은 한국어 감정사전을 효과적인 방법으로 구축할 수 있을 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.392-412
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• 2018

Due to the semantic gap problem across different modalities, automatically retrieval from multimedia information still faces a main challenge. It is desirable to provide an effective joint model to bridge the gap and organize the relationships between them. In this work, we develop a deep image annotation and classification by fusing multi-modal semantic topics (DAC_mmst) model, which has the capacity for finding visual and non-visual topics by jointly modeling the image and loosely related text for deep image annotation while simultaneously learning and predicting the class label. More specifically, DAC_mmst depends on a non-parametric Bayesian model for estimating the best number of visual topics that can perfectly explain the image. To evaluate the effectiveness of our proposed algorithm, we collect a real-world dataset to conduct various experiments. The experimental results show our proposed DAC_mmst performs favorably in perplexity, image annotation and classification accuracy, comparing to several state-of-the-art methods.

• The Journal of the Acoustical Society of Korea
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• v.18 no.6
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• pp.84-88
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• 1999

The accurate estimation of segmental duration is crucial for natural-sounding text-to-speech synthesis. For predicting Korean segmental durations, conventional methods utilized phonemic context, part-of-speech context and locational information in prosodic phrase. In this paper, the tonal information of phonemes is employed for more accurate prediction. After defining two non-boundary tones and six boundary tones, we annotated the tonal label on each syllable of 400 sentences. To predict segmental duration using tonal information, we constructed neural networks with a real-valued output node predicting phonemic duration and trained them by backpropagation algorithm. Experimental results showed that the proposed features are effective for predicting Korean segmental durations, and we got 0.863 correlation coefficient of the observed durations and predicted ones.

• Journal of the Korea Safety Management & Science
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• v.17 no.2
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• pp.153-160
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• 2015

This study tried to investigate the difference of the legibility in different age groups (young and old) with same near visual acuity and to provide preliminary guidelines for the text sizes in the pesticides products based on the legibility experiments. To achieve the objective, legibility evaluation experiments were conducted to test the effects of different age(20s, 50-60s), gender, font type(thick gothic-type and fine gothic-type), thickness (plain and bold), and number of syllables(2 and 3 syllables) in the same near visual acuity(0.6). The results showed that legibility 50s was higher than 20s. And 20s are appealed higher discomfort than 50s. Considering these experimental results, it was recommended that the 14 pt or larger characters (100% readable size) should be used the important information such as toxicity, and the minimum character size was 7 pt (50% readable size) for other information.