• Journal of the Korean Society for Library and Information Science
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• v.44 no.2
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• pp.117-137
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• 2010

The purposes of this study are to review the historical development of food and culture in the KDC (Korean Decimal Classification) and to propose improvements of the KDC to classify materials of food and culture by using the KDC effectively. First of all, changes of classification numbers and headings related to food and culture from the 1st edition to the 5th edition of the KDC were examined. Recent books about food and culture were examined and were classified according to the latest edition of the KDC. Several problems were found including a lack of headings, including notes in food and culture, a lack of headings about Korean foods in particular and reflections about various foods from other countries, a lack of detailed relative headings, and the remaining western oriented headings. Other classification systems about food and culture were analyzed and it was found that there was a need to have new headings for classifying Korean traditional foods and table services, new formats for a relative index, detailed notes, and changes in western oriented headings.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.21 no.1
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• pp.43-57
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• 2010

The purposes of this study are to examine how food and culture and Korean foods are reflected in the classification systems and to propose improvements of DDC to classify various subjects related to the materials of food and culture. For the study, six classification systems - DDC(Dewey Decimal Classification), UDC(Universal Decimal Classification), LCC(Library of Congress Classification), KDC(Korean Decimal Classification), NDC (Nippon Decimal Classification), China Library Classification - were analyzed in aspects of eating and drinking customs, eating etiquette, nutrition and diet, food and drink, meal and table service, beverage technology, and food technology. As a result, there were few headings about Korean food in six classification systems and it was necessary for DDC to have new headings for classifying Korean and Asian traditional foods and table services. Due to the literary warrant in classification systems, it is required to publish and disseminate various Korean food recipes and publications to add new headings or notes in future classification systems.

• Journal of the Korean Society for information Management
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• v.27 no.4
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• pp.49-69
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• 2010

Library classification system is based upon academic disciplines, However, it is difficult to classify for Internet resources due to its lack of up-to-datedness and practicality. Especially, headings of Korean Decimal Classification need to reflect practical aspects and it should be also developed for classification of web based resources. The purposes of this study are to analyze the structures of directory classifications in Internet resources and to suggest additional headings of KDC as a practical library classification as well as a classification system for internet resources. Directory classification systems of Naver, Yahoo, Kyobo Internet book store, Amazon were selected and their food and culture subjects were analyzed for this study. The headings of KDC were compared to them and new possible headings were suggested with reference of NDC and DDC in the area of food and culture. This study provided a way of developing KDC for a classification system for Internet resources as well as library materials.

• Annual Conference on Human and Language Technology
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• 2013.10a
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• pp.102-106
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• 2013

레스토랑 리뷰 분석을 위해서는 음식메뉴 개체명 인식이 매우 중요하다. 그러나 현재의 개체명 사전을 이용하여 리뷰 분석을 할 경우 구체적이고 복잡한 음식메뉴명을 표현하는데 충분하지 않으며 지속적인 업데이트가 힘들어 새로운 트렌드의 음식 메뉴명 등이 반영되지 않는 문제가 있다. 본 논문에서는 레스토랑 전문 사이트와 레시피 제공 사이트에서 각 레스토랑의 메뉴 정보와 음식명 등을 래퍼기반 웹 크롤러로 수집하였다. 그런 다음 빈도수가 낮은 음식메뉴와 레스토랑 온라인 리뷰에서 쓰이지 않는 음식메뉴를 제거하여 레스토랑 음식 메뉴 사전을 자동으로 구축하였다. 그리고 레스토랑 온라인 리뷰 문서를 이용해 음식 메뉴 사전의 엔티티들이 어느 유형의 레스토랑 리뷰에서 발견되는지를 찾아 빈도수를 구하고 분류 정보에 따른 비율을 사전에 추가하였다. 이 정보를 이용해 여러 분류 유형에 해당되는 음식메뉴를 구분할 수 있다. 실험 결과 한국관광공사 외국어 용례사전의 음식 메뉴명은 1,104개의 메뉴가 실제 레스토랑 리뷰에서 쓰인데 비해 본 논문에서 구축한 사전은 1,602개의 메뉴가 실제 레스토랑 리뷰에서 쓰여 498개의 어휘가 더 구성되어 있는 것을 확인 할 수 있었다. 이와 아울러, 자동으로 수집한 메뉴의 정확도와 재현율을 분석한다. 실험 결과 정확률은 96.2였고 재현율은 78.4, F-Score는 86.4였다.

• The Journal of Bigdata
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• v.8 no.2
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• pp.133-140
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• 2023

This study was conducted with the aim of improving the food image classification model of a health care application targeting Koreans in their twenties. 546,194 images were collected from the Public Data Portal and AI Hub, and 175 food classes were constructed. The ResNet artificial intelligence model was trained and validated. Additionally, we deeply investigated the reasons for the relatively lower recognition accuracy of the actual food images, and we attempted various methods to optimize the model's performance as a solution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1441-1446
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• 2022

Modern people can access various information about food recipes very easily on the Internet or social media. As the supply of food recipes increases, it is difficult to find a suitable recipe for each user in the overflowing information. As such, the need to provide information by reflecting users' requirements has increased, and research related to food recipes and cooking recommendations is becoming active. In addition, the Internet, video, and application markets using this are also rapidly activating. In this study, in order to classify recipes from the user's perspective of food recipe users, the user's review data was applied with the k-mean clustering technique, which is unsupervised learning, and a "food recipe classification model" was derived. As a result, it was classified into a total of 25 clusters including information needed by many users, such as specific purposes and cooking stages.

• Food Industry
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• s.77
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• pp.83-89
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• 1984

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.43-44
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• 2022

기존 자연어 처리 모델은 문맥 단위 단어 임베딩을 처리하지 못하는 한계점을 가지고 있는 한편 최근 BERT 기반 사전학습 모델들은 문장 단위 임베딩이 가능하고 사전학습을 통해 학습 효율이 비약적으로 개선되었다는 특징이 있다. 본 논문에서는 사전학습 언어 모델들을 이용하여 음식점, 배달전문점 등 음식 업종에서 발생한 고객 발화 의도를 분류하고 모델별 성능을 비교하여 최적의 모델을 제안하고자 한다. 연구결과, 사전학습 모델의 한국어 코퍼스와 Vocab 사이즈가 클수록 고객의 발화 의도를 잘 예측하였다. 한편, 본 연구에서 발화자의 의도를 크게 문의와 요청으로 구분하여 진행하였는데, 문의와 요청의 큰 차이점인 '물음표'를 제거한 후 성능을 비교해본 결과, 물음표가 존재할 때 발화자 의도 예측에 좋은 성능을 보였다. 이를 통해 음식 업종에서 발화자의 의도를 예측하는 시스템을 개발하고 챗봇 시스템 등에 활용한다면, 발화자의 의도에 적합한 서비스를 정확하게 적시에 제공할 수 있을 것으로 기대한다.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.377-378
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• 2017

본 연구는 외국인 유학생대상 한국세시풍속문화 체험교육콘텐츠 개발의 필요성을 제고하여 한국문화교육 실시에 필요한 실용적인 체험교육콘텐츠 개발에 목적을 두었다. 한국세시풍속문화 체험교육콘텐츠는 계절별로 나누었으며. 콘텐츠별 분류는 의례, 음식, 놀이, 예술, 축제 등의 다섯 분류로 나누었다. 콘텐츠의 분류는 오덕(五德)의 의미를 담아 의례에는 예(禮), 음식에는 의(義), 놀이에는 지(智), 예술에는 인(仁), 축제에는 신(信) 등으로 재조직하여 분류하였고, 그에 맞추어 교육콘텐츠를 개발하였다. 본 연구를 통해 개발된 외국인 유학생 대상 한국세시풍속문화 체험교육콘텐츠가 실용적으로 확산되기를 기대한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.451-459
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• 2013

In this paper we analyze the Korean foods and sensory adjectives that can be used for the taste expression of corresponding food based on the fuzzy relation. In order to construct fuzzy relation we gathered and chose 87 related Korean adjectives for expressing not only taste but also smell from foods. After then we performed a sensory evaluation for 51 Korean foods with 20 subjects to check the proper adjectives when they take a food. Based on the data collected by the evaluation a fuzzy relation is constructed and used for the analysis of the properties of food and adjectives. In addition the composition of the fuzzy relation provides the fuzzy tolerance(compatibility) relation among foods as well as that among adjectives. From the fuzzy complete ${\alpha}$-cover of the relations we could explore the taxonomy of food or adjectives. We expect that the fuzzy relation-based scheme in the paper can be utilized for analysis of the sensory adjectives like smelling and tactile sensation.