• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권8호
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• pp.2178-2198
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• 2024

Graph-based document models have good capabilities to reveal inter-dependencies among unstructured text data. Natural language processing (NLP) systems that use such models as an intermediate representation have shown good performance. This paper proposes a novel fuzzy graph-based document model and to demonstrate its effectiveness by applying fuzzy logic tools for text summarization. The proposed system accepts a text document as input and identifies some of its sentence level features, namely sentence position, sentence length, numerical data, thematic word, proper noun, title feature, upper case feature, and sentence similarity. The fuzzy membership value of each feature is computed from the sentences. We also propose a novel algorithm to construct the fuzzy graph as an intermediate representation of the input document. The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric is used to evaluate the model. The evaluation based on different quality metrics was also performed to verify the effectiveness of the model. The ANOVA test confirms the hypothesis that the proposed model improves the summarizer performance by 10% when compared with the state-of-the-art summarizers employing alternate intermediate representations for the input text.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.683-689
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• 2003

Information retrieval must be able to search the most suitable document that user need from document set. If foretell document adaptedness by similarity degree about QL(Query Language) of document, documents that search person does not require are searched. In this paper, showed that can search the most suitable document on user's request searching document of the whole space using genetic algorithm and used knowledge-base operator to solve various model's problem.

• 정보과학회 논문지
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• 제44권12호
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• pp.1290-1295
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• 2017

문서의 텍스트를 바탕으로 각 문서가 속한 분류를 찾아내는 문서 분류는 자연어 처리의 기본 분야 중 하나로 주제 분류, 감정 분류 등 다양한 분야에 이용될 수 있다. 문서를 분류하기 위한 신경망 모델은 크게 단어를 기본 단위로 다루는 단어 수준 모델과 문자를 기본 단위로 다루는 문자 수준 모델로 나누어진다. 본 논문에서는 문서를 분류하는 신경망 모델의 성능을 향상시키기 위하여 문자 수준과 단어 수준의 모델을 혼합한 신경망 모델을 제안한다. 제안하는 모델은 각 단어에 대하여 문자 수준의 신경망 모델로 인코딩한 정보와 단어들의 정보를 저장하고 있는 단어 임베딩 행렬의 정보를 결합하여 각 단어에 대한 특징 벡터를 만든다. 추출된 단어들에 대한 특징 벡터를 바탕으로, 주의(attention) 메커니즘을 이용한 순환 신경망을 단어 수준과 문장 수준에 각각 적용하는 계층적 신경망 구조를 통해 문서를 분류한다. 제안한 모델에 대하여 실생활 데이터를 바탕으로 한 실험으로 효용성을 검증한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.393-396
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• 2003

This paper proposes a method to speed up the document image binarization using a water flow model. The proposed method extracts the region of interest (ROI) around characters from a document image and restricts pouring water onto a 3-dimensional terrain surface of an image only within the ROI. The amount of water to be filled into a local valley is determined automatically depending on its depth and slope. Then, the proposed method accumulates weighted water not only on the locally lowest position but also on its neighbors. Finally, the depth of each pond is adaptively thresholded for robust character segmentation. Experimental results on real document images shows that the proposed method has attained good binarization performance as well as remarkably reduced processing time compared with that of the existing method based on a water flow model.

• 디지털산업정보학회논문지
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• 제7권1호
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• pp.41-49
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• 2011

We designed a security improved document DRM for protecting document based military information which is transmitted in the military information system environment. The user should be could not access document which not related to his/her role and duty, and must view the only document appropriate for his/her role and security level according to the security level of document. We improved the security of document DRM by adding to the access control module in DRM server. Our system allows operation mode authorizations for the document, considering the user's role & security level and the security level of document. And it prevents indiscriminate access to the document and damage the confidentiality and integrity of information.

• 한국정보통신학회논문지
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• 제12권5호
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• pp.828-836
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• 2008

본 논문에서는 다양한 상호작용을 지원하는 SMIL 2.0 멀티미디어 문서를 확장된 시간 구간 모델에 기반하여 문서의 시간 관계를 검증하는 방법을 제안한다. SMIL 2.0 문서는 기존 SMIL 문서 보다 비동기적인 시간관계의 기술방법을 추가되어 객체간 다양한 상호작용 관계를 제공하지만, 이러한 상호작용 관계에 의한 시간 관계에서 모순이 발생할 가능성이 높다. 그러므로 제안한 검증 방법은 SMIL 2.0 문서를 기존 시간 구간 모델을 확장하여 상호작용 관계를 함수적 관계로 표현하고 객체간 상호작용 관계와 시간 구간을 이용하여 SMIL 문서를 검증한다. 제안한 검증 방법의 적용 예로는 다자간 상호작용 콘텐츠, 온라인 교육 등이다.

• 한국정보처리학회논문지
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• 제6권5호
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• pp.1153-1165
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• 1999

In various fields of information technology, it is growing up the needs about dynamic content management systems to store and manage SGML(Standard Generalized Markup language) documents in a database system. In this paper, we consider the issue of storing SGML documents that having complex hierarchical structure into a database system, and then propose a data model based on ODMG(Object Database Management Group) object model in order to store SGML documents without loss of information. Because the proposed data model reflects physical element structure and logical entity structure of SGML documents, it is able to store the SGML document in a database system at the system at the element- level granularity without any information loss. And also the proposed data model can be adapted among ODMG-compliant object database management systems. Finally, we will discuss on the implementation details of SGML repository system supports the functionality of automatic database schema creation for any DTD(Document Type Definition0, the functionality of storing the SGML document, the functionality of dynamic document assembly from stored database objects to SGML document, and the functionality of indexing and searching for database objects.

• 한국컴퓨터정보학회논문지
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• 제13권4호
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• pp.11-18
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• 2008

문서군집은 정보검색의 많은 응용분야에 사용되는 중요한 문서 분석 방법이다. 본 논문은 비음수 행렬 분해(NMF, non-negative matrix factorization)를 기반한 용어 가중치 재산정 방법을 이용하여서 사용자의 요구에 적합한 군집결과를 얻도록 하는 새로운 군집모델을 제안한다. 제안된 모델은 군집형태에 대한 사용자 요구와 기계에 의한 군집 형태의 차이를 최소화하기 위하여 사용자 피드백에 의한 가중치가 재계산된 용어를 이용한다. 또한 제안방법은 용어의 가중치 재계산과 문서군집에 문서집합의 내부구조를 나타내는 의미특징행렬과 의미변수행렬 이용하여 문서군집의 성능을 높일 수 있다. 실험결과 제안방법을 적용한 문서군집방법이 적용하지 않은 문서군 방법에 비하여 좋은 성능을 보인다.

• 한국멀티미디어학회논문지
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• 제13권12호
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• pp.1786-1797
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• 2010

Automatic understanding of contents in document image is a very hard problem due to involvement with mathematically challenging problems originated mainly from the over-determined system induced by document segmentation process. In both academic and industrial areas, there have been incessant and various efforts to improve core parts of content retrieval technologies by the means of separating out segmentation related issues using semi-structured document, e.g., invoice,. In this paper we proposed classification models for text lines on invoice document in which text lines were clustered into the five categories in accordance with their contents: purchase order header, invoice header, summary header, surcharge header, purchase items. Our investigation was concentrated on the performance of machine learning based models in aspect of linear-discriminant-analysis (LDA) and non-LDA (logic based). In the group of LDA, na$\"{\i}$ve baysian, k-nearest neighbor, and SVM were used, in the group of non LDA, decision tree, random forest, and boost were used. We described the details of feature vector construction and the selection processes of the model and the parameter including training and validation. We also presented the experimental results of comparison on training/classification error levels for the models employed.

• Journal of information and communication convergence engineering
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• 제11권3호
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• pp.179-184
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• 2013

Traditional clustering methods are usually based on the bag-of-words (BOW) model. A disadvantage of the BOW model is that it ignores the semantic relationship among terms in the data set. To resolve this problem, ontology or matrix factorization approaches are usually used. However, a major problem of the ontology approach is that it is usually difficult to find a comprehensive ontology that can cover all the concepts mentioned in a collection. This paper proposes a new document clustering method using semantic features and fuzzy relations for solving the problems of ontology and matrix factorization approaches. The proposed method can improve the quality of document clustering because the clustered documents use fuzzy relation values between semantic features and terms to distinguish clearly among dissimilar documents in clusters. The selected cluster label terms can represent the inherent structure of a document set better by using semantic features based on non-negative matrix factorization, which is used in document clustering. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.