• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.345-351
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• 2021

This study is designed to determine how to identify misleading news articles based on natural language processing on Artificial Intelligence & Bigdata. A misleading news discrimination system and method on natural language processing is initiated according to an embodiment of this study. The natural language processing-based misleading news identification system, which monitors the misleading vocabulary database, Internet news articles, collects misleading news articles, extracts them from the titles of the collected misleading news articles, and stores them in the misleading vocabulary database. Therefore, the use of the misleading news article identification system and methods in this study does not take much time to judge because only relatively short news titles are morphed analyzed, and the use of a misleading vocabulary database provides an effect on identifying misleading articles that attract readers with exaggerated or suggestive phrases. For the aim of our study, we propose news article identification methods in natural language processing on Artificial Intelligence & Bigdata.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.535-548
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• 2022

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.2
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• pp.108-115
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• 2023

Alongside the rapid development of AI technology, which is stepping in to do tasks more quickly and less prone to error than humans can, the possibility for MI (multiple intelligence) development warrants equal attention and fervor. Recent theories of human intelligence point beyond standard cognitive capacity, IQ, and shine a light on the other unique potentials naturally endowed to humans. The applicability of MI to aviation is discussed, suggesting that it is important to consider ways to integrate MI development techniques into pilot education and training. Experiential starting points are discussed.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.144-150
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• 2023

With progressive advancements in Man-made consciousness (computer based intelligence) and Profound Learning (DL), contributing altogether to Normal Language Handling (NLP), the precision and nature of Machine Interpretation (MT) has worked on complex. There is a discussion, but that its no time like the present the human interpretation became immaterial or excess. All things considered, human flaws are consistently dealt with by its own creations. With the utilization of brain networks in machine interpretation, its been as of late guaranteed that keen frameworks can now decipher at standard with human interpreters. In any case, simulated intelligence is as yet not without any trace of issues related with handling of a language, let be the intricacies and complexities common of interpretation. Then, at that point, comes the innate predispositions while planning smart frameworks. How we plan these frameworks relies upon what our identity is, subsequently setting in a one-sided perspective and social encounters. Given the variety of language designs and societies they address, their taking care of by keen machines, even with profound learning abilities, with human proficiency looks exceptionally far-fetched, at any rate, for the time being.

• Journal of Oriental Neuropsychiatry
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• v.19 no.1
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• pp.97-105
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• 2008

Objective : The objective of this study is to investigate the intellectual characteristics of the specific language impairment(SLI) and the borderline intelligence-language disorder (BI-LD). Method : 30 Children participated in this study, IS children with SLI(K-WISC-ill FIQ above 85, Test of Problem Solving score below -1.25SD, verbal comprehension factor index of K-WISC III below 80), 14 children with BI-LD(K-WISC-ill FIQ $70^{\sim}85$, Test of Problem Solving score below -1.25SD, verbal comprehension factor index of K-WISC III below 80). All students were evaluated with K-WISC III, Test of Problem Solving. full-scale IQ (FSIQ), \ verbal intelligence quotient (VIQ), Verbal Comprehension Index, and Test of Problem Solving score were compared between two groups. Result : All subtests scores of PIQ in the SLI were significantly higher than those in the BI-LD. there was no significant difference in the subtests scores of VIQ. In the VIQ subtests, Information, Arithmetic, Comprehension score were higher in the SLI compared to the BI-LD, but the score of Similarities and Vocabulary were similar between two groups. Conclusion: These results suggest that inspite of the difference of PIQ, SLI and BI-LD have similar language abilities, and there are some different intellectual characteristics between SLI and BI-LD

• Journal of the Korean Society for Library and Information Science
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• v.14
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• pp.67-92
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• 1987

In this paper, artificial intelligence applications in library and information science are reviewed. Especially, natural language processing and expert systems are represented as the two major application areas. In natural language processing, natural language interface systems and .question-answering systems are discussed in detail with some specific examples. In the second part of the paper, online search intermidiary systems, reference expert systems, classification and cataloging expert systems are described as possible expert systems to be developed in libraries and information systems. As a conclusion, implications of the artificial intelligence applications for librarians and information scientists are suggested.

• ETRI Journal
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• v.46 no.1
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• pp.137-153
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• 2024

This study introduces a new approach in Korean morphological analysis combining dictionary-based techniques with Transformer-based deep learning models. The key innovation is the use of a BERT-based reranking system, significantly enhancing the accuracy of traditional morphological analysis. The method generates multiple suboptimal paths, then employs BERT models for reranking, leveraging their advanced language comprehension. Results show remarkable performance improvements, with the first-stage reranking achieving over 20% improvement in error reduction rate compared with existing models. The second stage, using another BERT variant, further increases this improvement to over 30%. This indicates a significant leap in accuracy, validating the effectiveness of merging dictionary-based analysis with contemporary deep learning. The study suggests future exploration in refined integrations of dictionary and deep learning methods as well as using probabilistic models for enhanced morphological analysis. This hybrid approach sets a new benchmark in the field and offers insights for similar challenges in language processing applications.

• Proceedings of the Korean Society for Language and Information Conference
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• 1996.02a
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• pp.363-372
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• 1996

• Korean Journal of Radiology
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• v.24 no.8
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• pp.715-718
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• 2023

• Journal of The Korean Association of Information Education
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• v.23 no.2
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• pp.189-196
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• 2019

Artificial intelligence has begun to be used in various parts of our lives, and recently its sphere has been expanding. However, students tend to find it difficult to recognize artificial intelligence technology because education on artificial intelligence is not being conducted on elementary school students. This paper examined the teaching programming language and artificial intelligence teaching methods, and looked at the changes in students' attitudes toward artificial intelligence technology by conducting education on artificial intelligence. To this end, education on block-type programming language-based artificial intelligence technology was provided to students' level. And we looked at students' attitudes toward artificial intelligence technology through a single group pre-postmortem. As a result, it brought about significant improvements in interest in artificial intelligence, possible access to artificial intelligence technology and the need for education on artificial intelligence technology in schools.