• Journal of the Korea Computer Industry Society
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• v.3 no.8
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• pp.1039-1048
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• 2002

It is difficult and laborious to distinguish between the original and the plagiarism about the electrical documents or on-line received documents, specially student homeworks because in many case, the homeworks are written on the same subject. Existing methods are not appropriate to solve this problem, which find the most appropriate category using the expression frequency of index term in documents to be classified. In this paper, a new classification method was proposed to distinguish between the original and the plagiarism about documents which were written similarly which is based on the syntagma vector - except the similar syntagma tree structure and non-index term.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.649-655
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• 2013

Recently, researches are studied in developing efficient techniques for accessing, querying, and managing XML documents which are frequently used in the Internet. In this paper, we propose a new method to cluster XML documents efficiently. An element and an inclusion relationship of a XML document corresponds to a node and a level of the corresponding tree, respectively. Therefore, when two XML documents are similar then their nodes' names and levels of the corresponding trees are also similar. In this paper, we cluster XML documents by using nodes' names and levels of the corresponding tree as a feature of a document. The experiment shows that our proposed method has a good performance.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1400-1411
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• 2004

There are many cases that many XML documents have different DTDs in spite of having a similar structure and being logically same kind of document. For this reason, It occurs a problem that these XML documents have different database schema and are stored in different databases. So, in this paper, we propose an algorithm that unifies DTDs of these XML documents using the finite automata and the tree structure. The finite automata is suitable for representing repetition operators and connectors of DTD, and is a simple representation method for DTD. By using the finite automata, we are able to reduce the complexity of algorithm. And we apply a proposed algorithm to unify DTDs of science journals.

• The Journal of Korean Association of Computer Education
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• v.13 no.6
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• pp.79-89
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• 2010

In this paper, we proposed a method to find out similar sentences from documents to detect plagiarized documents. The proposed model adapts LSA and N-gram techniques to detect every type of Korean plagiarized sentence type. To evaluate the performance of the model, we constructed experimental data using students' essays on the same theme. Students made their essay by intentionally plagiarizing some reference documents. The experimental results showed that our proposed model outperforms the conventional N-gram model, Vector model, LSA model in precision, recall, and F measures.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.171-178
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• 2008

Since the XML standard was introduced by the W3C in 1998, documents that have been written in XML have been gradually increasing. Accordingly, several systems have been developed in order to efficiently manage and retrieve massive XML documents. BitCube-a bitmap indexing system-is a representative system for this field of research. Based on the bitmap indexing technique, the path bitmap indexing system(LH06), which performs the clustering of similar paths, improved the problem that the existing BitCube system could not solve, namely, determining similar paths. The path bitmap indexing system has the advantage of a higher retrieval speed in not only exactly matched path searching but also similar path searching. However, the similarity calculation algorithm of this system has a few particular problems. Consequently, it sometimes cannot calculate the similarity even though some of two paths have extremely similar relationships; further, it results in an increment in the number of meaningless clusters. In this paper, we have proposed a novel method that clustering, the similarity between the paths in order to solve these problems. The proposed system yields a stable result for clustering, and it obtains a high score in clustering precision during a performance evaluation against LH06.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.7
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• pp.23-33
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• 1996

This paper deals with the design of a recursively-indexed binary code for facsimile soruces and its performance. Sources used here are run-lengths of white pixels form higher-resolution facsimile. The modified huffman code used for G.3 facsimile is chosen for the performance comparison. Experiments confirm the fact that recursive indexing preserves the entropy of a memoryless geometric source: the entropy of recursively-indexed physical surce iwth roughly geometric distributin remains within 2% of the empirical source entropy. The designed recursively-indexed binary codes consist of a code applied to text-type documents and to graphics - type documents is compared iwth that of the modified huffman code. Numerical resutls show that the modified huffman code performs well for text-type documents and not equally well for graphics-tyep documents. On the other hand, recursively-indexed binary codes have shown a better performance for graphics-type documents whose distribution are similar to a geometric distribution. Specifically, the code rates of recursively-indexed binary codes with 60 codewords are from 8% to 20% of the empirical source entropy smaller than that of th emodified huffman code with 91 codewords.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.27-35
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• 2013

Recently, as Social Network Services(SNS) are becoming more popular, much research has been doing on analyzing public opinions from SNS. One of the most important tasks for solving such a problem is to separate opinion(subjective) documents from others(e.g. objective documents) in SNS. In this paper, we propose a new method of retrieving the opinion documents from Twitter. The reason why it is not easy to search or classify the opinion documents in Twitter is due to a lack of publicly available Twitter documents for training. To tackle the problem, at first, we build a machine-learned model for sentiment classification using the external documents similar to Twitter, and then modify the model to separate the opinion documents from Twitter. Experimental results show that proposed method can be applied successfully in opinion classification.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2108-2115
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• 2000

The conventional Boolean retrieval systems based on vector spae model can provide the results of retrieval fast, they can't reflect exactly user's retrieval purpose including semantic information. Consequently, the results of retrieval process are very different from those users expected. This fact forces users to waste much time for finding expected documents among retrieved documents. In his paper, we designed a bayesian SOM(Self-Organizing feature Maps) in combination with bayesian statistical method and Kohonen network as a kind of unsupervised learning, then perform classifying documents depending on the semantic similarity to user query in real time. If it is difficult to observe statistical characteristics as there are less than 30 documents for clustering, the number of documents must be increased to at least 50. Also, to give high rank to the documents which is most similar to user query semantically among generalized classifications for generalized clusters, we find the similarity by means of Kohonen centroid of each document classification and adjust the secondary rank depending on the similarity.

• The Journal of Korean Association of Computer Education
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• v.11 no.3
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• pp.67-80
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• 2008

Various document categorization methods have been studied to provide a user with an effective way of browsing a large scale of documents. They do compares set of documents into groups of semantically similar documents automatically. However, the automatic categorization method suffers from low accuracy. This thesis proposes a semi-automatic document categorization method based on the domains of documents. Each documents is belongs to its initial domain. All the documents in each domain are recursively clustered in a level-wise manner, so that the category tree of the documents can be founded. To find the clusters of documents, the stop-word of each document is removed on the document frequency of a word in the domain. For each cluster, its cluster keywords are extracted based on the common keywords among the documents, and are used as the category of the domain. Recursively, each cluster is regarded as a specified domain and the same procedure is repeated until it is terminated by a user. In each level of clustering, a user can adjust any incorrectly clustered documents to improve the accuracy of the document categorization.

• Journal of Korean Library and Information Science Society
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• v.32 no.3
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• pp.307-330
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• 2001

This study experimented the performance of categorization methods using the kNN classifier. Most sample based automatic text categorization techniques like the kNN classifier reduces the feature set of the training documents. We sought to find out which percentage reductions in the feature set would result in high performances. In addition, the kNN classifier has to find the k number of training documents most similar to the test documents in the training documents. We sought to verify the most appropriate k value through experiments.