• Journal of Software Assessment and Valuation
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• v.14 no.2
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• pp.35-44
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• 2018

Tools for detecting cross-language clones usually compare abstract-syntax-tree representations of source code, which lacks scalability. In order to compare large source code to a practical level, we need a similarity checking technique that works on a token level basis. In this paper, we define common tokens that represent all tokens commonly used in programming languages of different paradigms. Each source code of different language is then transformed into the list of common tokens that are compared. Experimental results using exEyes show that our proposed method using common tokens is effective in detecting cross-language clones.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.4
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• pp.340-345
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• 2018

The Compared to general sentences, the Equation uses a complex structure and various characters and symbols, so that it is not possible to input all the character sets by simply inputting a keyboard. Therefore, the editor is implemented in a text editor such as Hangul or Word. In order to express the Equation properly, it is necessary to have the learner information which can be meaningful to interpret the syntax. Even if a character is input, it can be represented by another expression depending on the relationship between the size and the position. In other words, the form of the expression is expressed as a tree model considering the relationship between characters and symbols such as the position and size to be expressed. As a field of character recognition application, a technique of recognizing characters or symbols(code) has been widely known, but a method of inputting and interpreting a Equation requires a more complicated analysis process than a general text. In this paper, we have implemented a Equation recognizer that recognizes characters in expressions and quickly analyzes the position and size of expressions.

• Journal of KIISE
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• v.41 no.11
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• pp.992-1005
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• 2014

Coreference resolution finds all expressions that refer to the same entity in a document. Coreference resolution is important for information extraction, document classification, document summary, and question answering system. In this paper, we adapt Stanford's Multi-pass sieve system, the one of the best model of rule based coreference resolution to Korean. In this paper, all noun phrases are considered to mentions. Also, unlike Stanford's Multi-pass sieve system, the dependency parse tree is used for mention extraction, a Korean acronym list is built 'dynamically'. In addition, we propose a method that calculates weights by applying transitive properties of centers of the centering theory when refer Korean pronoun. The experiments show that our system obtains MUC 59.0%, $B_3$ 59.5%, Ceafe 63.5%, and CoNLL(Mean) 60.7%.

• Korean Journal of Cognitive Science
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• v.15 no.2
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• pp.33-42
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• 2004

There are many syntactic similarities between Japanese and Korean language. These similarities enable us to build Japanese-Korean translation systems without depending cm sophisticated syntactic analysis and semantic analysis. To further improve translation accuracy, we have been developing a Japanese-Korean translation system using these similarities for several years. However, there still remain some problems with regard to translation of inflected words, processing of multi-translatable words and so on. In this paper, we propose a new method for Japanese-Koran machine translation by using the relationships of two neighboring words. To solve the problems, we investigate the connection rules of auxiliary verb priority. And we design the translation table, which consists of entry tables and connection form tables. for unambiguous words, we can translate a Japanese word to the corresponding Korean word in terms of direct-matching method by consulting the only entry table. Otherwise we have to evaluate the connection value computed from connection form tables and then we can select the most appropriate target word.

• Journal of Korea Multimedia Society
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• v.7 no.7
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• pp.998-1008
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• 2004

There are many syntactic similarities between Japanese and Korean languages. Using these similarities, we can make out the Japanese-Korean translation system without most of syntactic analysis and semantic analysis. To improve the translation rates greatly, we have been developing the Japanese-Korean translation system using these similarities from several years ago. However, the system remains some problems such as a translation of inflected words, processing of multi-translatable words and so on. In this paper, we suggest the new method of Japanese-Korean translation by using relations of two neighboring words. To solve the problems, we investigated the connection rules of auxiliary verbs priority. And we design the translation table which is consists of entry tables and connection forms tables. A case of only one translation word, we can translate a Korean to Japanese by direct matching method use of only entry table, otherwise we have to evaluate the connection value by connection forms tables and then we can select the best translation word.

• Journal of KIISE:Software and Applications
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• v.28 no.11
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• pp.822-832
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• 2001

We have implemented an inductive learning system that learns PROSPECTOR-rule-style classification rules from sets of examples. In our a approach, a genetic algorithm is used in which a population consists of rule-sets and rule-sets generate offspring through the exchange of rules relying on genetic operators such as crossover, mutation, and inversion operators. In this paper, we describe our learning environment centering on the syntactic structure and meaning of classification rules, the structure of a population, and the implementation of genetic operators. We also present a method to evaluate the performance of rules and a heuristic approach to generate rules, which are developed to implement mutation operators more efficiently. Moreover, a method to construct a classification system using multiple learned rule-sets to enhance the performance of a classification system is also explained. The performance of our learning system is compared with other learning algorithms, such as neural networks and decision tree algorithms, using various data sets.