• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.30-35
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• 2008

The purpose of this study is to analyze and define user's emotional satisfaction factors to the PC OS GUI image. The study is to investigate the relationship between PC OS GUI Image and Sensitive Vocabula교 based on user's emotional preference. 47 user preferred sensitive words are collected by the initial survey. Through the similarity test, 47 words are narrowed down to 20 comprehend words. The semantic differential methods is used in the final survey with 5 step questionnaire. From this process, user preferred the GUI design that is vocabularized as Clear, Easy, Safety, Stability. Additionally, the result shows that the image of Clear is related to Safety and the image of Easy is related to Stability. The result of the study could be used in design PC OS GUI as base data.

• Annual Conference on Human and Language Technology
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• 1992.10a
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• pp.73-81
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• 1992

한 언어에 대한 포괄적인 언어정보 데이타베이스의 구축에 있어서는 수집된 텍스트에 대한 상세한 문법정보의 주석이 일차적 작업 대상이 된다. 이는 통사적 정보가 단순히 구문 분석상의 문제들을 해결하기 위한 정보를 제공해주는 것일 뿐 아니라 형태소 해석 및 문장 의미의 파악등 자연언어 이해시스템 전반의 성능을 향상시키는 데에 중요한 물을 차지하기 때문이다. 각개 단어의 문법적 기능에 대한 주석은 사전적 정의에 따른다면 "품사"로 표현할 수 있을 것이다. 그런데 품사는 각개 단어가 지니는 고유한 어휘의미적 정보이기보다는 구문구조에 의존적인 양상을 보인다. 이는 사전에 따라서 각개 단어에 대한 품사 정보가 달리 나타나는 점에서도 간취할 수 있는데, 한편으로 한국어 언어정보 데이타베이스 구축을 위한 문법적 주석에 있어서는 기존 사전의 품사정보에만 의존할 수는 없다는 문제점이 제기된다. 따라서 각 어휘들의 구문정보(흑은 품사정보)를 어떻게 기술할 것인가가 해결되어야 하는 것이다. 본 연구에서는 일차적으로 각 어휘들의 문장 안에서의 기능을 바탕으로 한 주석체계를 설정하고 그에 따라서 약 12만개의 문장에 대한 일차적 형식화를 수작업으로 처리하였다. 이는 향후 자동적으로 문법적 주석이 가능하도록 해주는 시스템의 개발을 지원하기 위한 언어정보의 수집에 목적을 둔 것인데, 이를 통해서 기존 국어사전에서의 언어정보상의 미비점을 수정 보완할 몇 가지 근거를 마련할 수 있었다.

• Journal of Information Management
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• v.32 no.2
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• pp.26-39
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• 2001

Various controlled vocabularies such as thesaurus and classification used for effective information retrieval contain different terms in expressing the same concept or meaning. National Library of Medicine has developed the Unified Medical Language System(UMLS) to solve the problems of information retrieval and integration resulted from the difference of concepts between different sources. The UMLS development was initiated in 1982 as a long-term project, and the 2001 edition of the UMLS consists of three parts : Metathesaurus, Semantic Network, and SPECIALIST Lexicon. This paper reviews background and structure of the UMLS including applications in PubMed, NLM Gateway.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.392-403
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• 2008

User reviews are valuable information that can be used for various purposes. In particular, the product reviews on online shopping sites are important information which can directly affect the purchasing decision of the customers. In this paper, we present our design and implementation of a system for summarizing the customer's opinion and the features of each product by analyzing reviews on a commercial shopping site. During the analysis process, several natural language processing(NLP) techniques and the semantic dictionary were used. The semantic dictionary contains vocabularies that are used to express product features and customer's opinions. And it was constructed in semi-automatic way with the help of the tool we implemented. Furthermore, we discuss how to handle the vocabularies that have different meanings according to the context. We analyzed 1796 reviews about 20 products of 2 categories collected from an actual shopping site and implemented a novel ranking system. We obtained 88.94% for precision and 47.92% for recall on extracting opinion expression, which means our system can be applicable for real use.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1101-1112
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• 2004

In this paper, we propose a Web page weighting scheme using the user preference in each sense of query word to improve the performance of Web search. Generally search engines assign weights to a web page by using relevancy only, which is obtained by comparing the query word and the words in a web page. In the information retrieval from huge data such as the Web, simple word comparison cannot distinguish important documents because there exist too many documents with similar relevancy In this paper we implement a WordNet-based user interface that helps to distinguish different senses of query word, and constructed a search engine in which the implicit evaluations by multiple users are reflected in ranking by accumulating the number of clicks. In accumulating click counts, they are stored separately according to senses, so that more accurate search is possible. The experimental results with several keywords show that the precision of proposed system is improved compared to conventional search engines.

• School Mathematics
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• v.18 no.4
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• pp.903-922
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• 2016

This paper examined vocabulary in elementary mathematics textbooks and workbooks for the grade band of 3~4. This study focused on the adequacy of vocabulary using 9-degree of semantic difficulty. The results of this study showed that about three to eight percent of vocabulary with difficulty for students in grades 3~4 was employed. Whereas some difficult vocabularies were used along with the picture that helps students understand their meanings, others were presented without any illustrative tools. The most frequent vocabularies in the main activities of the mathematics textbooks included 'investigate', 'represent', 'number', 'how many', and 'method'. Based on these results, this study provides textbook writers with implications on what to further consider in dealing with vocabulary in instructional materials.

• The Journal of the Korea Contents Association
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• v.9 no.2
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• pp.18-26
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• 2009

A hybrid part-of-speech tagging approaches may be robust, easily extendable, and accurate because they can have the advantages of both statistical approach and rule-based approach. But conventional hybrid part-of-speech tagging systems hardly resolve some morphological ambiguities which can't be resolved by statistical information. It is because the coverage of rules is narrow. So, we define disambiguation rules for individual ambiguous word based on syntax and semantics of surround words. We select words from which the top 50% of ambiguities are occurred in Sejong corpus and build 1,814 rules for them. The accuracy of our hybrid part-of-speech tagging system using those rules is 98.28%.

• Journal of KIISE:Software and Applications
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• v.37 no.1
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• pp.60-73
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• 2010

This paper aims to describe KorLexClas 1.5 which provides us with a very large list of Korean numeral classifiers, and with the co-occurring noun categories that select each numeral classifier. Differently from KorLex of other POS, of which the structure depends largely on their reference model (Princeton WordNet), KorLexClas 1.0 and its extended version 1.5 adopt a direct building method. They demand a considerable time and expert knowledge to establish the hierarchies of numeral classifiers and the relationships between lexical items. For the efficiency of construction as well as the reliability of KorLexClas 1.5, we use following processes: (1) to use various language resources while their cross-checking for the selection of classifier candidates; (2) to extend the list of numeral classifiers by using a shallow parsing techniques; (3) to set up the hierarchies of the numeral classifiers based on the previous linguistic studies; and (4) to determine LUB(Least Upper Bound) of the numeral classifiers in KorLexNoun 1.5. The last process provides the open list of the co-occurring nouns for KorLexClas 1.5 with the extensibility. KorLexClas 1.5 is expected to be used in a variety of NLP applications, including MT.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.325-332
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• 2011

Social Network Services(SNS) such as Twitter, Facebook and Myspace have gained popularity worldwide. Especially, sentiment analysis of SNS users' sentence is very important since it is very useful in the opinion mining. In this paper, we propose a new sentiment classification method of sentences which contains formal and informal vocabulary such as emoticons, and newly coined words. Previous methods used only formal vocabulary to classify sentiments of sentences. However, these methods are not quite effective because internet users use sentences that contain informal vocabulary. In addition, we construct suggest to construct domain sentiment vocabulary because the same word may represent different sentiments in different domains. Feature vectors are extracted from the sentiment vocabulary information and classified by Support Vector Machine(SVM). Our proposed method shows good performance in classification accuracy.

• Language and Information
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• v.9 no.1
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• pp.1-18
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• 2005

In Korean terminologies, adjectival terms are rare, and the meaning and function associated with adjectives in Indo-European languages are often realized instead in noun form. However, the rarer adjectival terms we, the more they are used in restrictive and repetitive ways in specialized domains. Thus, it is important to distinguish the different senses of these terms. In this work, focusing on semantic modeling in terminology, we distinguish the different senses of adjectival medical terms by applying lexico-semantic criteria (L'Homme, 2004a) and by classifying the semantic category of the arguments of the adjective (Bae and others, 2002). The result not only contributes to enriching medical terminology, but also empirically demonstrates a method for distinguishing the different senses of adjectival medical terms. In this work, we obtained an average of 1.854 senses for each term. We used the KAIST corpus, composed of medical texts (1,500,000 eojeols), and a group of texts on various subjects (40,000,000 eojeols)