• The KIPS Transactions:PartB
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• v.11B no.5
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• pp.563-572
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• 2004

Sentence ion is a simplification of a sentence preserving its communicative function. It accomplishes sentence revision and concept ion simultaneously. Sentence revision is a method that resolves the discrepancy between human's thoughts and its expressed semantic in sentences. Concept ion is an expression of general ideas acquired from the common elements of concepts. Sentence ion selects the main constituents of given sentences and describes the upper concepts of them with detecting their semantic information. This enables sen fence revision and concept ion simultaneously. In this paper, a syntactic parser LGPI+ and an ontology OfN are utilized for sentence ion. Sentence abstracter SABOT makes use of LGPI+ and OfN. SABOT processes the result of parsing and selects the candidate words for sentence ion. This paper computes the sentence recall of the main sentences and the topic hit ratio of the selected sentences with the text understanding system using sentence ion. The sources are 58 paragraphs in 23 stories. As a result of it, the sentence recall is about .54 ～ 72% and the topic hit ratio is about 76 ～ 86%. This paper verified that sentence ion enables sentence revision that can select the topic sentences of a given text efficiently and concept ion that can improve the depth of text understanding.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.8
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• pp.317-326
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• 2013

In this paper, we present an effective method for comparing control flow graphs which represent static structures of binary programs. To compare control flow graphs, we measure similarities by comparing instructions and syntactic information contained in basic blocks. In addition, we also consider similarities of edges, which represent control flows between basic blocks, by edge extension. Based on the comparison results of basic blocks and edges, we match most similar basic blocks in two control flow graphs, and then calculate the similarity between control flow graphs. We evaluate the proposed edge extension method in real world Java programs with respect to structural similarities of their control flow graphs. To compare the performance of the proposed method, we also performed experiments with a previous structural comparison for control flow graphs. From the experimental results, the proposed method is evaluated to have enough distinction ability between control flow graphs which have different structural characteristics. Although the method takes more time than previous method, it is evaluated to be more resilient than previous method in comparing control flow graphs which have similar structural characteristics. Control flow graph can be effectively used in program analysis and understanding, and the proposed method is expected to be applied to various areas, such as code optimization, detection of similar code, and detection of code plagiarism.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.35-42
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• 2013

Developing English-Korean machine translation system requires the construction of information about the languages, and the amount of information in English-Korean transfer dictionary is especially critical to the translation quality. Newly created words are out-of-vocabulary words and they appear as they are in the translated sentence, which decreases the translation quality. Also, compound nouns make lexical and syntactic analysis complex and it is difficult to accurately translate compound nouns due to the lack of information in the transfer dictionary. In order to improve the translation quality of English-Korean machine translation, we must continuously expand the information of the English-Korean transfer dictionary by collecting the out-of-vocabulary words and the compound nouns frequently used. This paper proposes a method for expanding of the transfer dictionary, which consists of constructing corpus from internet newspapers, extracting the words which are not in the existing dictionary and the frequently used compound nouns, attaching meaning to the extracted words, and integrating with the transfer dictionary. We also develop the tool supporting the expansion of the transfer dictionary. The expansion of the dictionary information is critical to improving the machine translation system but requires much human efforts. The developed tool can be useful for continuously expanding the transfer dictionary, and so it is expected to contribute to enhancing the translation quality.

• Korean Journal of Cognitive Science
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• v.21 no.1
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• pp.95-126
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• 2010

This study proposes a bottom-up and inductive manual mapping methodology for integrating two heterogenous fine-grained ontologies which were built by a top-down and deductive methodology, namely the Sejong semantic classes (SJSC) and the upper nodes in KorLexNoun 1.5 (KLN), for HLP applications. It also discusses various problematics in the mapping processes of two language resources caused by their heterogeneity and proposes the solutions. The mapping methodology of heterogeneous fine-grained ontologies uses terminal nodes of SJSC and Least Upper Bounds (LUB) of KLN as basic mapping units. Mapping procedures are as follows: first, the mapping candidate groups are decided by the lexfollocorrelation between the synsets of KLN and the noun senses of Sejong Noun Dfotionaeci(SJND) which are classified according to SJSC. Secondly, the meanings of the candidate groups are precisely disambiguated by linguistic information provided by the two ontologies, i.e. the hierarchicllostructures, the definitions, and the exae les. Thirdly, the level of LUB is determined by applying the appropriate predicates and definitions of SJSC to the upper-lower and sister nodes of the candidate LUB. Fourthly, the mapping possibility ic inthe terminal node of SJSC is judged by che aring hierarchicllorelations of the two ontologies. Finally, the ituorrect synsets of KLN and terminologiollocandidate groups are excluded in the mapping. This study positively uses various language information described in each ontology for establishing the mapping criteria, and it is indeed the advantage of the fine-grained manual mapping. The result using the proposed methodology shows that 6,487 LUBs are mapped with 474 terminal and non-terminal nodes of SJSC, excluding the multiple mapped nodes, and that 88,255 nodes of KLN are mapped including all lower-level nodes of the mapped LUBs. The total mapping coverage is 97.91% of KLN synsets. This result can be applied in many elaborate syntactic and semantic analyses for Korean language processing.