• Journal of the Korea Computer Industry Society
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• v.3 no.8
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• pp.963-980
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• 2002

Software reengineering is making various research for solutions against problem of maintain existing systems. Reengineering has a meaning of development of software on exizting systems through the reverse engineering auf forward engineering. Most of the important concepts used in reengineering is composition that is restructuring of the existing objects. Is there a compiler that can compile a program written in a traditional procedural language (like C or Pascal) and generate a Java bytecode, rather than an executable code that runs oかy on the machine it was compiled (such as an a.out file on a Unix machine)\ulcorner This type of compiler may be very handy for today's computing environment of heterogeneous networks. In this paper we present a software system that does this job at the binary-to-binary level. It takes the compiled binary code of a procedural language and translates it into Java bytecode. To do this, we first translate into an assembler code called Jasmin [7] that is a human-readable representation of Java bytecode. Then the Jasmin assembler converts it into real Java bytecode. The system is not a compiler because it does not start at the source level. We believe this kind of translator is even more useful than a compiler because most of the executable code that is available for sharing does not come with source programs. Of course, it works only if the format of the executable binary code is known. This translation process consists of three major stages: (1) analysis stage that identifies the language constructs in the given binary code, (2) initialization stage where variables and objects are located, classified, and initialized, and (3) mapping stage that maps the given binary code into a Jasmin assembler code that is then converted to Java bytecode.

• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.321-326
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• 2020

This paper is to analyze the language education policy in the context of multilingualism. As the majority of the population are multilingual, language policy should be centered on the multilingual speakers as the norm, and multilingual language policy is the best route which we can follow as a language policy in education. The motivation and legitimacy of the multilingual policies are suggested in terms of 6 different perspectives: identity, sustainability, equity, World Englishes, machine translation, and Universal Grammar (UG). As a model of language policy, the English-Plus (i.e., English+n) policy and similarly the Korean-Plus (i.e., Korean+n) policy are suggested to be the most appropriate language policies in the field of education in America and Korea respectively. These plus policies aim at bilingual fluency in both the native language and other foreign languages that are constitutive of the multilingualism of the country in which the bilingualism is treated as a variant of multilingualism. In a period of convergence and diversity in the 4^th Industrial Revolution, language diversity and multilingual policy should be considered as a right to be protected or as a resource to be conserved rather than as a problem to be solved.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.169-178
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• 2022

Morphemes are most primitive units in a language that lose their original meaning when segmented into smaller parts. In Korean, a sentence is a sequence of eojeols (words) separated by spaces. Each eojeol comprises one or more morphemes. Korean morphological analysis (KMA) is to divide eojeols in a given Korean sentence into morpheme units. It also includes assigning appropriate part-of-speech(POS) tags to the resulting morphemes. KMA is one of the most important tasks in Korean natural language processing (NLP). Improving the performance of KMA is closely related to increasing performance of Korean NLP tasks. Recent research on KMA has begun to adopt the approach of machine translation (MT) models. MT is to convert a sequence (sentence) of units of one domain into a sequence (sentence) of units of another domain. Neural machine translation (NMT) stands for the approaches of MT that exploit neural network models. From a perspective of MT, KMA is to transform an input sequence of units belonging to the eojeol domain into a sequence of units in the morpheme domain. In this paper, we propose a deep learning model for KMA. The backbone of our model is based on the BERT-fused model which was shown to achieve high performance on NMT. The BERT-fused model utilizes Transformer, a representative model employed by NMT, and BERT which is a language representation model that has enabled a significant advance in NLP. The experimental results show that our model achieves 98.24 F1-Score.

• The Journal of Bigdata
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• v.6 no.1
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• pp.63-81
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• 2021

The performance of natural language processing is rapidly improving due to the recent development and application of machine learning and deep learning technologies, and as a result, the field of application is expanding. In particular, as the demand for analysis on unstructured text data increases, interest in NLP(Natural Language Processing) is also increasing. However, due to the complexity and difficulty of the natural language preprocessing process and machine learning and deep learning theories, there are still high barriers to the use of natural language processing. In this paper, for an overall understanding of NLP, by examining the main fields of NLP that are currently being actively researched and the current state of major technologies centered on machine learning and deep learning, We want to provide a foundation to understand and utilize NLP more easily. Therefore, we investigated the change of NLP in AI(artificial intelligence) through the changes of the taxonomy of AI technology. The main areas of NLP which consists of language model, text classification, text generation, document summarization, question answering and machine translation were explained with state of the art deep learning models. In addition, major deep learning models utilized in NLP were explained, and data sets and evaluation measures for performance evaluation were summarized. We hope researchers who want to utilize NLP for various purposes in their field be able to understand the overall technical status and the main technologies of NLP through this paper.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4C
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• pp.494-504
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• 2004

The researches about gaze detection have been much developed with many applications. Most previous researches only rely on image processing algorithm, so they take much processing time and have many constraints. In our work, we implement it with a computer vision system setting a IR-LED based single camera. To detect the gaze position, we locate facial features, which is effectively performed with IR-LED based camera and SVM(Support Vector Machine). When a user gazes at a position of monitor, we can compute the 3D positions of those features based on 3D rotation and translation estimation and affine transform. Finally, the gaze position by the facial movements is computed from the normal vector of the plane determined by those computed 3D positions of features. In addition, we use a trained neural network to detect the gaze position by eye's movement. As experimental results, we can obtain the facial and eye gaze position on a monitor and the gaze position accuracy between the computed positions and the real ones is about 4.2 cm of RMS error.

• Progress in Medical Physics
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• v.31 no.3
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• pp.111-123
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• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• Annual Conference on Human and Language Technology
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• 1995.10a
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• pp.195-207
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• 1995

일본어는 한자, 히라가나, 가다가나 등 다양한 종류의 문자를 사용하며 이들의 혼용 비율이 매우 높아 띄어쓰기를 하지 않아도 문서의 가독성을 유지한다. ICOT 사전, EDR 사전, ATLAS I/JK사전 등 기존의 전자 사전에서 복합 자종의 표제어가 차지하는 비율(한자+히라가나의 표제어 제외)은 평균 8.8%로 그 수가 매우 작다. 따라서, 문장 내에서 자종의 변화는 단어를 구분하는 하나의 delimiter로 이용될 수 있다. 본 시스템에서는 형태소 분석의 전단계로 전처리기를 두어 자종정보(character type information)에 의한 fragment 분리 및 예외 단어, 정형표현 처리를 수행하며 각 fragment 의 형태소 분석 방법을 제시한다. 형태소 분석기는 전처리기의 처리 결과를 입력받아 각각의 fragment를 전처리기가 제시한 분석 방법에 따라 분석하여 입력 문장의 가능한 모든 분석을 추출한다. 이 방법은 불필요한 사전 탐색과 접속 체크 회수를 줄여 분석 성능을 향상시킨다.

• Annual Conference on Human and Language Technology
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• 1997.10a
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• pp.249-254
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• 1997

영어 웹 문서를 한국어로 기계번역을 하기 위해서는 HTML 태그를 번역 대상 문장과 분리하는 처리가 필요하다. HTML 태그를 단순히 제거하는 것이 아니라 대상 문장의 기계번역이 종료된 후에 같은 형태의 한국어 웹 문서로 복원하기 위한 방안이 마련 되어야 한다. 또한 문서 전처리기에서는 영어 형태소해석기의 성능을 높이기 위하여 번역 단위가 되는 문장의 인식 및 분리, 타이틀의 처리, 나열된 단어의 처리, 하이픈 처리, 고유명사 인식, 특수 문자 처리, 대소문자 정규화, 날짜 인식 등을 처리하여 문서의 정규화를 수행한다.

• Annual Conference on Human and Language Technology
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• 1997.10a
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• pp.271-276
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• 1997

대화체는 문어체와는 달리 화자와 청자 사이의 질의/응답으로 이루어진 형태의 문장들을 가지며, 생략과 대용어가 빈번히 발생하는 특징을 갖는다. 이러한 대화 형태에서 어떠한 한 문장에는 화자가 전달하고자 하는 의도를 포함하고 있다. 이러한 대화체 문장들을 번역하는 것은 단순한 언어적 분석에 의한 번역으로서는 많은 번역상의 오류가 발생하게 된다. 따라서 대화체 문장들의 올바른 번역을 위해서는 대화의 상황을 반영하는 문맥 정보가 부가적으로 요구된다. 본 연구에서는 이러한 문맥 정보로서 화행을 사용하여 대화체 기계번역을 수행하고자 한다. 화행(Speech Act)이란 화자에 의해 의도되어 발화 속에 포함된 언어적 행위를 나타내며, 이러한 화행을 분석함으로써 화자의 의도를 파악하고 이를 통해 올바른 번역을 수행할 수 있게 된다. 본 기계번역 시스템에 포함된 화행 분석 과정에서는 대화를 화행으로 모델링한 담화 문법과 유사한 형태의 재귀적 대화 전이망(Recursive Dialog Transition Network)을 사용하게 된다. 본 논문에서는 호텔 예약 영역에서의 기계번역 시스템에 대한 간단한 소개와 화행의 종류 및 분석 방법과 이를 통한 기계번역 방식에 대해 살펴보도록 하겠다.

• Annual Conference on Human and Language Technology
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• 2003.10d
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• pp.241-245
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• 2003

본 논문은 기계번역 시스템의 성능평가를 위한 '핵심어 전달율 측정' 방안에 대해서 기술한다. 기계번역 시스템의 성능평가는 두 가지 측면으로 고려될 수 있다. 첫 번째는 객관적인 평가로 IBM에서 주창한 BLEU score 측정이나 NIST의 NIST score 측정이 그 예이다. 객관적인 평가는 평가자의 주관적인 판단이나 언어적인 특성을 배제한 방법으로 프로그램을 통해 자동으로 fluency와 adequacy를 측정하여 성능을 평가한다. 다음은 주관적인 평가이다. 주관적인 평가는 평가자의 평가를 통해 번역의 품질을 평가하는 방법이다. 주관적 평가 방법의 대표적인 것으로는 NESPOLE이나 LDC가 있다. 주관적인 평가는 평가자의 정확한 판단으로 신뢰할만한 성능평가 결과를 도출하지만, 시간과 비용이 많이 들고, 재사용할 수 없다는 단점이 있다. 본 논문에서는 이러한 문제를 해결하기 위해, 번역대상 문장에서 핵심어를 추출하고, 그 핵심어가 기계번역 시스템의 수행결과에 전달된 정도를 자동으로 측정하는 새로운 평가방법인 '핵심어 전달율 측정' 방안을 제안한다. 이는 성능평가의 비용과 시간을 절약하고, 주관적 평가와 유사한 신뢰성 있는 평가결과를 얻을 수 있는 좋은 지표가 될 수 있을 것으로 기대한다.