• Journal of the Korea Society of Computer and Information
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• v.17 no.9
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• pp.39-47
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• 2012

The opinion of user on a certain product is expressed in positive/negative sentiments for specific features of it. In some cases, they are expressed for a holistic part of homogeneous specific features, or expressed for product itself. Therefore, in the area of opinion mining, name of opinion features to be extracted are specific feature names, holonyms for theses specific features, and product names. However, when the opinion target is described with product name or holonym, sometimes it may not match feature name of opinion sentence to true opinion target intended by the reviewer. In this paper, we present a method to extract opinion targets from opinion sentences. Most importantly, we propose a method to extract true target from the feature names mismatched to a intended target. First, we extract candidate opinion pairs using dependency relation between words, and then select feature names frequently mismatched to opinion target. Each selected opinion feature name is replaced to a specific feature intended by the reviewer. Finally, in order to extract relevant opinion features from the whole candidate opinion pairs including modified opinion feature names, candidate opinion pairs are rearranged by the order of user's interest.

• Journal of The Korean Association For Science Education
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• v.37 no.1
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• pp.215-223
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• 2017

This study aims to find an effective way to present essential science concepts in national science curriculum through international comparisons. Next Generation Science Standard (US), Ontario Science Curriculum (Canada), Australia Science Curriculum, and British/English Science Curriculum were selected for comparison. In science curriculum documents, these countries used terms such as 'Key ideas,' 'Big ideas,' 'Key concepts,' 'Disciplinary core ideas.' and 'Fundamental concepts' to present essential concepts of science. This study reviewed the characteristics of the meaning, the status, and the role of essential concepts country by country. The result shows essential concepts have been used with different meanings and statutes in each case. Furthermore, various roles were performed through essential concepts in order to organize their science curriculum. From these foreign nation's cases, this study proposes several ways to present essential science concepts based on results. First, interdisciplinary integrated concepts were needed to organize an integrated science curriculum. In science curriculum documents of the United States, Canada, Australia and England, two types of terms were used in order to structuralize an integrated science curriculum. Second, essential concepts should include concepts related with function and value as well as scientific knowledge. Third, essential concepts need to be presented in such a way as to show specific contexts. Therefore, selecting appropriate contents and structure are needed to be able to improve the way to present essential concepts in Korea's educational environment.

• Journal of Korean Academy of Nursing
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• v.18 no.1
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• pp.26-33
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• 1988

내담자와 상담자간의 관계형성 추진에 관한 연구가 C. Rogers에 의해 시작된 이래, 돕는자 또는 상담자가 가져야 할 주요 조건으로서 공감, 존중, 온정, 확고부동함, 진지성, 자기노출, 직면반응 등을 들고 있으며 이중 둘 또는 세 요소 등을 선택하여 그 효과를 보고 있으나 역시 가장 주요한 요소로써는 공감을 들고 있다. 공감에 관한 연구는 그 본질의 정서적 측면, 인지적 측면 또는 복합적인 측면을 강조하면서 시도되고 있으나 간호원은 돕는자로써 환자의 문제해결을 위한 전수자적 역할을 해야한다는 점을 고려할때 간호현상에서의 공감에 관한 연구는 복합적인 측면을 강조하는 공감이 어 야 한다고 생각한다. 간호학자들도 간호원의 돕는 행위중 주요 요소로써 공감을 들고 있으며 특히 Lamonica는 공감측정을 위한 도구를 개발하였으며 공감이란 환자가 간호원이 환자의 입장을 이해하고 도와준다는 사실을 인지하고 그 고마움을 표현하는 것을 의미한다고 하였다. 본 연구자는 간호원-환자간의 촉진적 관계형성을 위한 교육내용 개발에 대한 기본 연구로써 한국인의 공감 정도를 측정할 수 있는 도구개발의 중요성을 느껴 Lamonica 의 공감측정 도구를 번역하게 되었다. 본 연구의 구체적인 목적은 미국문화권에서 사용되는 공감측정 도구가 한국인에게 적합하고 의미있게 번역되었는지를 확인하고 또한 한국인이 인지한 공감에 대한 탐색을 하는데 있다. 위 목적달성을 위하여 횡문화적 연구과정을 통한 개념분석, 도구 개발에 대한 통계분석을 시도하였다. 한국인의 공감 개념 분석을 위하여 미국 텍사스 오스틴에 있는 한국인에게 공감의 뜻, 동의어, 어떤 경우에 공감을 느꼈는지, 어떤 경우에 비공감적임을 느꼈는지를 물은 결과 한국인이 갖는 공감의 의미는 미국인의 것과 유사하지만 그 표현방법의 차이가 있음을 알게 되었다. 따라서 두 국가에서 사용되는 공감의 의미가 유사하고 또한 간호학자인 Lamonica가 개발한 공감측정 도구를 한국인에게 사용하는데 무리가 없을 것으로 판단되었다. 도구의 번역은 텍사스 주립대학 박사과정 지원생인 임상 심리 학자에게 의뢰하고 그 정확성을 판단하기 위해 인간을 대상으로 하는 학문을 연구하는 한국인(간호학, 사회학, 신문방송, 광고학, 심리학 전공)에게 그 정확성 유무를 물어 최고 27점, 최하 9점중 22점 미만인 문항에 대해서는 미국 간호학자와 의논하여 수정ㆍ보완하였다. 그 후 일반인으로 간주되는 한국인에게 그 도구의 이해 여부를 확인한 후 통계분석을 시도하였다. 대상자는 미국 텍사스 오스틴에 거주하면서 한국을 떠난지 3년 미만인 성인 45명을 대상으로 하였다. 2차에 걸친 자료수집 과정상 5명의 자료는 분석 불가능하다고 판단되어 총 40명의 자료를 spss- X를 이용하여 cronbach's alpha, test-retest stability, intercorrelation matrix 분석을 통한 결과는 다음과 같다. 1) cronbach's alpha는 1차 .9353 2차 .9666으로써 문항의 동질성을 보였고, 3, 4주 간격으로 행한 test-retest stability는 .7619(p＝000)이였다. 2) 반면에 intercorrelation matrix에서는 역관계 또는 무관계를 보였으며 84문항중 26문항의 item-to-total correlation값이 .35미만이었고 이 중 16문항은 .30 미만이었다. 이들을 제외한 68문항과 58문항의 각각의 item-to-total correlation간은 .96이었고 test retest stability 역시 .76으로써 84문항 전체에 관한 값과 유사하였다. 3) 역상관 또는 무상관의 값을 보인 문항을 미국 간호학자와 재검토한 결과 본래 문항에서의 단어 의미가 복합적이거나 불분명한 것이었고 또는 미국 문화권에서 사용되는 특이한 용어임을 알 수 있었다. 따라서 한국인 공감 측정 도구의 타당성을 높이기 위해 역통역을 시도하였다. 그후 공감에 관한 연구를 하고 있는 미국 학자에게 그 정확성을 판단하여 최종적으로 58문항이 한국인 공감측정 도구로서 적합하다는 판단을 하였다. 위 결과를 통한 결론 및 제언은 다음과 같다. 인간의 행위는 조건화된 문화권에 따라 다를 수 있으며, 이것은 같은 현상을 인지하는데도 영향을 미치게 되며 본 연구와 같이 어떤 현상에 대한 횡문화적 연구는 그 행위를 이해하는데 도움을 준다. 그러나 한국에서 간호에 대한 연구가 한국적 토착화 과정에 있으므로 그 연구 방법이나 도구사용이 서구의 것을 도입해야 하는 입장을 고려할 때 도구번역 과정은 원래의 의미나 함축성을 내포한 번역이어야 하며 소홀히 해서는 안될 과정임을 재확인되었다. 또한 추후 연구로써 다양한 계층의 다수를 대상으로 한 한국인 공감 측정 도구의 타당성을 재확인해야 하며 요인분석을 시도할 필요성이 있다고 사려된다.

• Journal of KIISE
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• v.44 no.1
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• pp.51-56
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• 2017

The research area for solving question answering (QA) problems using artificial intelligence models is in a methodological transition period, and one such architecture, the dynamic memory network (DMN), is drawing attention for two key attributes: its attention mechanism defined by neural network operations and its modular architecture imitating cognition processes during QA of human. In this paper, we increased accuracy of the inferred answers, by adapting an automatic data augmentation method for lacking amount of training data, and by improving the ability of time perception. The experimental results showed that in the 1K-bAbI tasks, the modified DMN achieves 89.21% accuracy and passes twelve tasks which is 13.58% higher with passing four more tasks, as compared with one implementation of DMN. Additionally, DMN's word embedding vectors form strong clusters after training. Moreover, the number of episodic passes and that of supporting facts shows direct correlation, which affects the performance significantly.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.97-110
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• 2018

With the advent of the 4-th industrial revolution, manufacturing companies have increasing interests in the realization of smart manufacturing by utilizing their accumulated facilities data. However, most previous research dealt with the structured data such as sensor signals, and only a little focused on the unstructured data such as text, which actually comprises a large portion of the accumulated data. Therefore, we propose an association rule mining based facility error pattern extraction framework, where text data written by operators are analyzed. Specifically, phrases were extracted and utilized as a unit for text data analysis since a word, which normally used as a unit for text data analysis, is unable to deliver the technical meanings of facility errors. Performances of the proposed framework were evaluated by addressing a real-world case, and it is expected that the productivity of manufacturing companies will be enhanced by adopting the proposed framework.

• Journal of KIISE
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• v.42 no.4
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• pp.512-521
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• 2015

Internet users purchase goods on the Internet and express their positive or negative emotions of the goods in product reviews. Analysis of the product reviews become critical data to both potential consumers and to the decision making of enterprises. Therefore, the importance of opinion mining techniques which derive opinions by analyzing meaningful data from large numbers of Internet reviews. Existing studies were mostly based on comments written in English, yet analysis in Korean has not actively been done. Unlike English, Korean has characteristics of complex adjectives and suffixes. Existing studies did not consider the characteristics of the Internet language. This study proposes an emotional classification method which increases the accuracy of emotional classification by analyzing the characteristics of the Internet language connoting feelings. We can classify positive and negative comments about products automatically using the Internet emoticon. Also we can check the validity of the proposed algorithm through the result of high precision, recall and coverage for the evaluation of this method.

• Journal of KIISE
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• v.42 no.12
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• pp.1568-1574
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• 2015

Relation extraction plays a role in for the process of transforming a sentence into a form of knowledge base. In this paper, we focus on predicates in a sentence and aim to identify the relevant knowledge base properties required to elucidate the relationship between entities, which enables a computer to understand the meaning of a sentence more clearly. Distant Supervision is a well-known approach for relation extraction, and it performs lexicalization tasks for knowledge base properties by generating a large amount of labeled data automatically. In other words, the predicate in a sentence will be linked or mapped to the possible properties which are defined by some ontologies in the knowledge base. This lexical and ontological linking of information provides us with a way of generating structured information and a basis for enrichment of the knowledge base.

• Journal of the Korean Society for information Management
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• v.20 no.2
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• pp.114-133
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• 2003

Recently, the organizations related to cultural contents are gradually expanding access to cultural contents for general public through the distributed network. However, since cultural contents have different characteristics than general contents, the objects, the of cultural contents seldom contain the words generally used for organization and search of information. If the cultural contents system is created without any consideration of such differences. We cannot effectively identify and search resources. Moreover, because the names, expressions and meanings are different between metadata elements of various cultural contents, it is very difficult to interconnect or share information between different systems. In order to solve these problems, proper organization ad management of metadata is vital. In this studym we have comparatively analyzed the data elements of each format based on Dublin Core, EAD, VRA, CDWA, CIMI, and Object ID, the metadata formats approached from various aspects in the cultural contents area. Through this study, we tried to provide the basic materials for integration of cultural contents by securing interoperability of different metadata formats.

• Database Research
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• v.34 no.3
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• pp.3-13
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• 2018

Text classification is one of the text mining technologies that classifies a given textual document into its appropriate categories and is used in various fields such as spam email detection, news classification, question answering, emotional analysis, and chat bot. In general, the text classification system utilizes machine learning algorithms, and among a number of algorithms, naïve Bayes and support vector machine, which are suitable for text data, are known to have reasonable performance. Recently, with the development of deep learning technology, several researches on applying deep neural networks such as recurrent neural networks (RNN) and convolutional neural networks (CNN) have been introduced to improve the performance of text classification system. However, the current text classification techniques have not yet reached the perfect level of text classification. This paper focuses on the fact that the text data is expressed as a vector only with the word dimensions, which impairs the semantic information inherent in the text, and proposes a neural network architecture based upon the semantic tensor space model.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.749-758
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• 2004

In this paper, we propose a new method utilizing only raw corpus without additional human effort for disambiguation of target word selection in English-Korean machine translation. We use two data-driven techniques; one is the Latent Semantic Analysis(LSA) and the other the Probabilistic Latent Semantic Analysis(PLSA). These two techniques can represent complex semantic structures in given contexts like text passages. We construct linguistic semantic knowledge by using the two techniques and use the knowledge for target word selection in English-Korean machine translation. For target word selection, we utilize a grammatical relationship stored in a dictionary. We use k- nearest neighbor learning algorithm for the resolution of data sparseness Problem in target word selection and estimate the distance between instances based on these models. In experiments, we use TREC data of AP news for construction of latent semantic space and Wail Street Journal corpus for evaluation of target word selection. Through the Latent Semantic Analysis methods, the accuracy of target word selection has improved over 10% and PLSA has showed better accuracy than LSA method. finally we have showed the relatedness between the accuracy and two important factors ; one is dimensionality of latent space and k value of k-NT learning by using correlation calculation.