• Language and Information
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• v.14 no.1
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• pp.165-196
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• 2010

The primary goal of this paper is to propose methods of enriching two heterogeneous ontologies: Sejong Semantic Classes (SJSC) and KorLexNoun 1.5 (KLN). In order to achieve this goal, this study introduces the pros and cons of two ontologies, and analyzes the error patterns found during the fine-grained manual mapping processes between them. Error patterns can be classified into four types: (1) structural defectives involved in node branching, (2) errors in assigning the semantic classes, (3) deficiency in providing linguistic information, and (4) lack of the lexical units representing specific concepts. According to these error patterns, we propose different solutions in order to correct the node branching defectives and the semantic class assignment, to complement the deficiency of linguistic information, and to increase the number of lexical units suitably allotted to their corresponding concepts. Using the results of this study, we can obtain more enriched ontologies by correcting the defects and errors in each ontology, which will lead to the enhancement of practicality for syntactic and semantic analysis.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.10-20
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• 2010

This paper presents chunking strategy of a contiguous nouns sequence using semantic class. We call contiguous nouns which can be treated like a noun the compound noun phrase. We use noun pairs extracted from a syntactic tagged corpus and their semantic class pairs for chunking of the compound noun phrase. For reliability, these noun pairs and semantic classes are built from a syntactic tagged corpus and detailed dictionary in the Sejong corpus. The compound noun phrase of arbitrary length can also be chunked by these information. The 38,940 pairs of 'left noun - right noun', 65,629 pairs of 'left noun - semantic class of right noun', 46,094 pairs of 'semantic class of left noun - right noun', and 45,243 pairs of 'semantic class of left noun - semantic class of right noun' are used for compound noun phrase chunking. The test data are untrained 1,000 sentences with contiguous nouns of length more than 2randomly selected from Sejong morphological tagged corpus. Our experimental result is 86.89% precision, 80.48% recall, and 83.56% f-measure.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.67-76
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• 2019

In this paper, we propose an efficient object classification method based on semantic segmentation for multi-labeled image data. In addition to various pixel unit information and processing techniques such as color information, contour, contrast, and saturation included in image data, a detailed region in which each object is located is extracted as a meaningful unit and the experiment is conducted to reflect the result in the inference. We use a neural network that has been proven to perform well in image classification to understand which object is located where image data containing various class objects are located. Based on these researches, we aim to provide artificial intelligence services that can classify real-time detailed areas of complex images containing various objects in the future.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.75-89
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• 2021

In this paper, the results of analyzing the meaning of laughter in comedy movies of the late 1960s, focusing on comedians, Hee-Gap Kim, Bong-Seo Koo and Young-Chun Seo, are as follows. First, the narrative laughter of the comedians represents the generational conflict between the lower class and the upper class, the gender conflict between men and women, and the frustration of reality. Second the mechanism of creating laughter of the comedians shows ridicule of the conservative older generation, revelation of the immoral upper class, and caricature of the frustrated lower class. Third, the practical aspect of the comedians' laughter reveals cracks in tradition and modern, gaps in economic inequality, and expression of prohibited desires. Therefore, comedians Hee-Gap Kim, Bong-Seo Koo and Young-Chun Seo in comedy movies of the late 1960s show the semantic effect of laughter of internal/external boundaries and optimistic world views through the closed opening of informal culture and the conflict of characters/performers.

• 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.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.829-840
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• 2005

Word Sense Disambiguation(WSD) is one of the most difficult problem in Korean information processing. We propose a WSD model with the capability to filter semantic information using the specific characteristics in dictionary dictions, and nth added information, useful to sense determination, such as statistical, distance and case information. we propose a model, which can resolve the issues resulting from the scarcity of semantic information data based on the word hierarchy system (thesaurus) developed by Ulsan University's UOU Word Intelligent Network, a dictionary-based toxicological database. Among the WSD models elaborated by this study, the one using statistical information, distance and case information along with the thesaurus (hereinafter referred to as 'SDJ-X model') performed the best. In an experiment conducted on the sense-tagged corpus consisting of 1,500,000 eojeols, provided by the Sejong project, the SDJ-X model recorded improvements over the maximum frequency word sense determination (maximum frequency determination, MFC, accuracy baseline) of $18.87\%$ ($21.73\%$ for nouns and inter-eojeot distance weights by $10.49\%$ ($8.84\%$ for nouns, $11.51\%$ for verbs). Finally, the accuracy level of the SDJ-X model was higher than that recorded by the model using only statistical information, distance and case information, without the thesaurus by a margin of $6.12\%$ ($5.29\%$ for nouns, $6.64\%$ for verbs).