• The Journal of Korean Association of Computer Education
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• v.21 no.6
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• pp.83-92
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• 2018

The subjective question is appropriate for evaluation of deep thinking, but it is not easy to score. Since, regardless of same scoring criterion, the graders are able to produce different scores, we need the objective automatic evaluation system. However, the system has the problem of Korean analysis and comparison. This paper suggests the Korean syntactic analysis and subjective grading system using the syntactic-semantic tree comparator. This system is the hybrid grading system of word based and syntactic-semantic tree based grading. This system grades the answers on the subjective question using the syntactic-semantic comparator. This proposed system has the good result. This system will be utilized in Korean syntactic-semantic analysis, subjective question grading, and document classification.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.2
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• pp.53-60
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• 2020

Sentiment analysis is the automated process of understanding attitudes and opinions about a given topic from written or spoken text. One of the sentiment analysis approaches is a dictionary-based approach, in which a sentiment dictionary plays an much important role. In this paper, we propose a method to automatically generate Korean sentiment lexicon from the well-known English sentiment lexicon called VADER (Valence Aware Dictionary and sEntiment Reasoner). The proposed method consists of three steps. The first step is to build a Korean-English bilingual lexicon using a Korean-English parallel corpus. The bilingual lexicon is a set of pairs between VADER sentiment words and Korean morphemes as candidates of Korean sentiment words. The second step is to construct a bilingual words graph using the bilingual lexicon. The third step is to run the label propagation algorithm throughout the bilingual graph. Finally a new Korean sentiment lexicon is generated by repeatedly applying the propagation algorithm until the values of all vertices converge. Empirically, the dictionary-based sentiment classifier using the Korean sentiment lexicon outperforms machine learning-based approaches on the KMU sentiment corpus and the Naver sentiment corpus. In the future, we will apply the proposed approach to generate multilingual sentiment lexica.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.2
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• pp.49-59
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• 2007

This paper is about an system which can perform automatic semantic annotation to actualize "Semantic Web." Since it is impossible to tag numerous documents manually in the web, it is necessary to gather large Korean web documents as training data, and extract features by using natural language techniques and a thesaurus. After doing these, we constructed concept classifiers through the SVM (support vector machine) teaming algorithm. According to the characteristics of Korean language, morphological analysis and syntax analysis were used in this system to extract feature information. Based on these analyses, the concept code is mapped with Kadokawa thesaurus, which made it possible to map similar words and phrase to one concept code, to make training vectors. This contributed to rise the recall of our system. Results of the experiment show the system has a some possibility of semantic annotation.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.759-764
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• 2002

Although image occupies a large part of importance on the Web documents, there have not been many researches for analyzing and understanding it. Many Web images are used for carrying important information but others are not used for it. In this paper classify the Web images from presently served Web sites to erasable or non-erasable classes. based on machine learning methods. For this research, we have detected 16 special and rich features for Web images and experimented by using the Baysian and decision tree methods. As the results, F-measures of 87.09%, 82.72% were achived for each method and particularly, from the experiments to compare the effects of feature groups, it has proved that the added features on this study are very useful for Web image classification.

• Annual Conference on Human and Language Technology
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• 1998.10c
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• pp.253-260
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• 1998

한국어 문서에는 여러 어절을 검증해야만 처리할 수 있는 의미 오류와 문체 오류가 있다. 다수 어절 사이에 있는 오류는 부분 문장 분석을 한다. 논문에서는 의미 오류와 문체 오류를 처리할 때 어휘적 중의성 때문에 생기는 문제점을 제시하고 해결방법을 제안한다. 어휘적 중의성이란 한 단어가 두 가지 이상의 형태소 정보를 가짐을 뜻한다. 철자검사기와는 달리 문법 검사기에서는 어휘적 중의성을 제거하지 않으면 여러 가지 검사 오류가 발생한다. 이 논문에서는 의미, 문체 시스템에서 어휘적 중의성 때문에 검사 오류가 발생할 수 있는 과정을 크게 세 단계로 분류하였다. 연어 오류가 발생할 수 있는 검사단어가 어휘적 중의성을 가지면 표제어가 다른 규칙이 여러 개 존재한다. 이 때 규칙 선택 문제가 생긴다. 중의성 문제는 부분 문장분석 과정에서도 지배소와 의존소 사이의 의존관계를 정확하게 설정하기 어렵게 한다. 본 논문에서는 각 단계에서 발생한 문제를 최소화하여 문법 검사기의 성능을 향상시킨다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.295-298
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• 2001

본 논문에서는 CCD카메라로 입력 받은 다중 크기 및 활자체로 구성된 한글문서의 화상 데이터를 편집기에서 수정 가능한 문자로 변환시키는 시스템을 구현하였다. 먼저 Dynamic 이 진화 처리 과정을 거친 화상을 흑백 화소의 누적분포에 따라 문자단위로 분할한 후, 다양한 크기로 분할된 문자를 표준패턴 크기로 표준화 시켰다. 한글을 자소 간 공백 위치의 특징에 따라서 6가지 유형으로 분류한 후, 퍼지 이론을 접목시킨 원형 패턴 벡터 알고리즘을 사용해서 표준벡터와 입력된 글자의 특징벡터를 비교하여 문자로 인식하게 하였다. 각 6가지 유형에서 서로 다른 자소로 결합된 문자들을 30개 선정하여 여러 가지 활자체 및 크기에 적용해 본 결과, 모두 문서화가 가능함을 알 수 있었다.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1150-1159
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• 2000

This paper presents an automatic text categorization model that improves the accuracy by combining statistical and knowledge-based categorization methods. In our model we apply knowledge-based method first, and then apply statistical method on the text which are not categorized by knowledge-based method. By using this combined method, we can improve the accuracy of categorization while categorize all the texts without failure. For statistical categorization, the vector model with Inverted Category Frequency (ICF) weighting is used. For knowledge-based categorization, Phrasal Patterns and Keyword Sets are introduced to represent sentence patterns, and then pattern matching is performed. Experimental results on new articles show that the accuracy of categorization can be improved by combining the tow different categorization methods.

• Proceedings of the Safety Management and Science Conference
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• 2000.11a
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• pp.235-242
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• 2000

우리나라 경제 구조는 대부분의 수익이 반도체등 몇 가지 제품에 한정되어 있으며 이들 제품들은 이미 성숙기에 접어들고 있어 성장성을 기대하기 어려운 실정이다. 따라서 다양화하는 시장 수요에 대응하고, 첨단 기술을 개발하기 위해서는 창조성과 시장 변화에 유연한 벤처 비즈니스가 요구된다. 본 연구에서는 벤처비지니스중 통신산업분야의 품질시스템에 대하여 W사 사례연구를 통한 효율적이고 효과적인 구축방안을 제안하고자 한다. W사 ISO9001 품질시스템 요건 및 업무분장에 따른 문서와 기록을 기초로 CSU, ADSL, 광전송장비, HDSL, FDSU, T3 DSU등의 첨단 통신제품들을 품질시스템 유형별로 자체생산제품, 제휴판매제품, 도입판매제품으로 분류하고, 제품 특성별로 비교 분석하여 제품별 유연한 품질시스템 구축방안을 제시하고자 한다.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.83-96
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• 2018

Extracting keywords representing documents is very important because it can be used for automated services such as document search, classification, recommendation system as well as quickly transmitting document information. However, when extracting keywords based on the frequency of words appearing in a web site documents and graph algorithms based on the co-occurrence of words, the problem of containing various words that are not related to the topic potentially in the web page structure, There is a difficulty in extracting the semantic keyword due to the limit of the performance of the Korean tokenizer. In this paper, we propose a method to select candidate keywords based on semantic similarity, and solve the problem that semantic keyword can not be extracted and the accuracy of Korean tokenizer analysis is poor. Finally, we use the technique of extracting final semantic keywords through filtering process to remove inconsistent keywords. Experimental results through real web pages of small business show that the performance of the proposed method is improved by 34.52% over the statistical similarity based keyword selection technique. Therefore, it is confirmed that the performance of extracting keywords from documents is improved by considering semantic similarity between words and removing inconsistent keywords.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.76-85
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• 2011

In this paper, we propose a texture feature-based language identification using Gabor feature and wavelet-domain BDIP (block difference of inverse probabilities) and BVLC (block variance of local correlation coefficients) features. In the proposed method, Gabor and wavelet transforms are first applied to a test image. The wavelet subbands are next denoised by Donoho's soft-thresholding. The magnitude operator is then applied to the Gabor image and the BDIP and BVLC operators to the wavelet subbands. Moments for Gabor magnitude image and each subband of BDIP and BVLC are computed and fused into a feature vector. In classification, the WPCA (whitened principal component analysis) classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the test feature vector. Experimental results show that the proposed method yields excellent language identification with rather low feature dimension for a document image DB.