• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.241-249
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• 2022

The purpose of this study is to analyze and verify the effects of multicultural children's psychosocial adaptation, bicultural experience, parental support and parenting attitudes, and school activities on the development of Korean language ability using data from the second stage of the MAPS(Multicultural Adolescents Panel Study) using a structural equation model. The subjects of this study were 396 children from foreign families and mid-immigrant families, multicultural children who were enrolled in the fourth grade of elementary school in 2019. As a result of the study, it was found that psychosocial adaptation, bicultural experience, and school activities directly or indirectly significantly affect the ability of multicultural children to speak and understand Korean. In particular, it was found that school activities have a direct effect on the improvement of the Korean language ability of multicultural children, so it was understood that the support of friends and teachers should be treated as very important parts of educational activities in the educational field. These results mean that the most ideal educational environment that affects the development of Korean language skills must be necessarily reflected in the Korean language education policy for multicultural children.

• Korean Journal of English Language and Linguistics
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• v.4 no.2
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• pp.151-174
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• 2004

A historical investigation reveals that English preposition with underwent a change from OPPOSITION to ASSOCIATION and further to ACCOMPANIMENT, where the first stage shows peculiarity in that the two concepts involved comprise an unusual set to form an extensional chain. Intrigued by this oddity, this paper aims to investigate the semantic structure of English preposition with from a grammaticalization perspective. We review mechanisms and models of semantic change and evaluate their adequacy with the semantic structure and change shown by with. Drawing upon the observed fact that with underwent the apparent antonymic semantic change, it is argued that such semantic change mechanisms as metaphor, metonymy, subjectification, and generalization have difficulties explaining the change, and that only the Frame-of-Focus Variation can effectively account for this peculiar change type. In terms of semantic change models, we argue that the Bleaching Model cannot effectively provide an explanation; that the Loss and Gain Model has problems in explaining the motivation of change directions; that the Metonymic-Metaphoric Model cannot be assessed at the current level of investigation; and that the Overlap Model and the Prototype Extension Model excellently account for the macro-level changes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1442-1453
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• 2016

Now, there are prodigiously expanding worldwide economic networks in the information society, which require their social structural changes through technology innovations. This paper so tries to formally define a process-centered knowledge model to be used to analyze policy-making procedures on technology innovations. The eventual goal of the proposed knowledge model is to apply itself to analyze a topic network based upon composite keywords from a document written in a natural language format during the technology innovation procedures. Knowledge model is created to topic network that compositing driven keyword through text mining from natural language in document. And we show that the way of analyzing knowledge model and automatically generating feature keyword and relation properties into topic networks.

• Annual Conference on Human and Language Technology
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• 1999.10e
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• pp.343-350
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• 1999

본 논문은 한글에서 나타나는 Word Speriority Effect의 특성을 설명해 주는 한글의 글자 인지모델을 제안한다. 제안된 모델은 영어에서 나타나는 Word Superiority Effect를 설명하기 위해서 제안된 Interactive Activation Model을 기초로 한다. 우선은 영어에 맞도록 설계된 Interactive Activation Model을 한글에 적용할 수 있도록 수정하는 방법에 대해서 알아본다. 다음으로 한글에서 나타난 Word Superiority Effect의 특징과 그러한 특징을 기존의 Interactive Activation Model에 반영하기 위한 방법에 대해 알아본다. 제안된 방법을 이용해서 수정된 Interactive Activation Model을 컴퓨터로 구현해서 모의실험한 결과를 분석함으로써 제안된 모델의 타당성을 검증하게 된다.

• Phonetics and Speech Sciences
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• v.15 no.3
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• pp.69-74
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• 2023

This study introduces an innovative model for zero-shot voice conversion that utilizes the capabilities of HuBERT. Zero-shot voice conversion models can transform the speech of one speaker to mimic that of another, even when the model has not been exposed to the target speaker's voice during the training phase. Comprising five main components (HuBERT, feature encoder, flow, speaker encoder, and vocoder), the model offers remarkable performance across a range of scenarios. Notably, it excels in the challenging unseen-to-unseen voice-conversion tasks. The effectiveness of the model was assessed based on the mean opinion scores and similarity scores, reflecting high voice quality and similarity to the target speakers. This model demonstrates considerable promise for a range of real-world applications demanding high-quality voice conversion. This study sets a precedent in the exploration of HuBERT-based models for voice conversion, and presents new directions for future research in this domain. Despite its complexities, the robust performance of this model underscores the viability of HuBERT in advancing voice conversion technology, making it a significant contributor to the field.

• Korean Journal of Radiology
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• v.25 no.2
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• pp.126-133
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• 2024

Large language models (LLMs) have revolutionized the global landscape of technology beyond natural language processing. Owing to their extensive pre-training on vast datasets, contemporary LLMs can handle tasks ranging from general functionalities to domain-specific areas, such as radiology, without additional fine-tuning. General-purpose chatbots based on LLMs can optimize the efficiency of radiologists in terms of their professional work and research endeavors. Importantly, these LLMs are on a trajectory of rapid evolution, wherein challenges such as "hallucination," high training cost, and efficiency issues are addressed, along with the inclusion of multimodal inputs. In this review, we aim to offer conceptual knowledge and actionable guidance to radiologists interested in utilizing LLMs through a succinct overview of the topic and a summary of radiology-specific aspects, from the beginning to potential future directions.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2E
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• pp.3-8
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• 2001

In this paper, we will examine a Korean large vocabulary continuous speech recognition (LVCSR) system for broadcast news speech. The combined vowel and implosive unit is included in a phone set together with other short phone units in order to obtain a longer unit acoustic model. The effect of this unit is compared with conventional phone units. The dictionary units for language processing are automatically extracted from eojeols appearing in transcriptions. Triphone models are used for acoustic modeling and a trigram model is used for language modeling. Among three major speaker groups in news broadcasts-anchors, journalists and people (those other than anchors or journalists, who are being interviewed), the speech of anchors and journalists, which has a lot of noise, was used for testing and recognition.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.105.2-105
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• 2001

This paper studies continuous Korean Sign Language (KSL) recognition using color vision. In recognizing gesture words such as sign language, it is a very difficult to segment a continuous sign into individual sign words since the patterns are very complicated and diverse. To solve this problem, we disassemble the KSL into 18 hand motion classes according to their patterns and represent the sign words as some combination of hand motions. Observing the speed and the change of speed of hand motion and using state automata, we reject unintentional gesture motions such as preparatory motion and meaningless movement between sign words. To recognize 18 hand motion classes we adopt Hidden Markov Model (HMM). Using these methods, we recognize 5 KSL sentences and obtain 94% recognition ratio.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.389-390
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• 2023

본 논문에서는 파이썬 코딩 플랫폼에서의 LLM(Large Language Models)을 로직 및 문법 에러 확인, 디버깅 도구로 활용할 수 있는 시스템을 제안한다. 이 시스템은 사용자가 코딩 플랫폼에서 작성한 파이썬 코드와 함께 발생한 에러 문구 및 프롬프트를 LLM 모델에 입력함으로써 로직(문법) 에러를 식별하고 디버깅에 활용할 수 있다. 특히, 입문자를 고려해 프롬프트를 제한하여 사용의 편의성을 높인다. 이를 통해 파이썬 코딩 교육에서 입문자들의 학습 과정을 원활하게 진행할 수 있으며, 파이썬 코딩에 대한 진입 장벽을 낮출 수 있다.

• 대한상한금궤의학회지
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• v.14 no.1
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• pp.41-50
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• 2022

Objective : The purpose of this study is to explore the most suitable machine learning model algorithm for Shanghanlun diagnostic system classification using natural language processing (NLP). Methods : A total of 201 data items were collected from 『Shanghanlun』 and 『Clinical Shanghanlun』, 'Taeyangbyeong-gyeolhyung' and 'Eumyangyeokchahunobokbyeong' were excluded to prevent oversampling or undersampling. Data were pretreated using a twitter Korean tokenizer and trained by logistic regression, ridge regression, lasso regression, naive bayes classifier, decision tree, and random forest algorithms. The accuracy of the models were compared. Results : As a result of machine learning, ridge regression and naive Bayes classifier showed an accuracy of 0.843, logistic regression and random forest showed an accuracy of 0.804, and decision tree showed an accuracy of 0.745, while lasso regression showed an accuracy of 0.608. Conclusions : Ridge regression and naive Bayes classifier are suitable NLP machine learning models for the Shanghanlun diagnostic system classification.