• Annual Conference on Human and Language Technology
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• 2002.10e
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• pp.92-97
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• 2002

의미기반 정보 검색, 자연어 질의 응답, 지식 자동 습득, 담화 처리 등 높은 수준의 자연언어처리 시스템에서 의미처리를 위한 대용량의 지식 베이스가 필요하다. 이러한 지식 베이스 중에서 가장 기본적인 것이 워드넷이다. 이러한 워드넷을 이용함으로써 여러 의미 사이의 의미 유사도를 구할 수 있고, 속성을 물려받을 수 있기 때문에 비슷한 속성을 가진 의미들을 한꺼번에 다루는 데 유용하다. 본 논문에서는 기본 어휘를 바탕으로 기존의 명사 워드넷과 단일어 사전을 이용하여 한국어 동사 워드넷을 구축하는 방법을 제시한다. 본 논문에서 1차 작업을 통하여 구축한 동사 워드넷에는 동사 1,757개에 대한 4,717개의 의미(중복을 포함하면 모두 5,235개의 의미)를 포함하고 있으며 특별히 의미가 많이 편중된 14개의 개념에 속한 571개의 의미를 53개의 세부 개념으로 재분류하여 최종적으로 모두 767개의 계층적 개념으로 구성된 동사 워드넷이 만들어 졌다.

• Cross-Cultural Studies
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• v.31
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• pp.371-394
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• 2013

This study aims to know word forms and usages according to personal relationships of 'Kureru Kudasaru' by analyzing dialogues of female speakers. Novels of the Meiji period when there were attempts of a language revolution were mainly used for this study as well as novels of the Edo Period and the Taisho Period. Firstly, the number of examples according to gender differences in the novels was as follows. In case of 'Kureru', female speakers showed a high usage rate in the novels of the Edo period. 'Kureru' was mostly connected with female languages such as 'Naharu', 'Namasu', 'Nansu'. These expressions were not used in the novels of the Meiji Period and the Taisho Period. Although 'Okureru' and 'Okurenasaru' were used in the novels of the Meiji Period, the number of examples of 'Kureru' by female speakers was decreased in the novels of the Meiji Period and the Taisho Period. 'Kudasaru' was predominantly used by female speakers. Especially, female speakers used clearly to show vertical relationships in the novels of the Edo Period and"Doseishoseikatagi"of Meiji 10s. After"Ukigumo", the usage rate of female speakers was decreased but the usage rate of male speakers was increased. Gender differences became gradually smaller. Female speakers in the novels were increased from geisha and relatives such as wife, sister, mother and children to young women, teacher and student. Aspects of benefactive verbs' usages could be summarized as follows. Female speakers at licensed quarters used clearer and more typical expressions according to vertical relationships and gender differences in the novels of The Edo Period than the novels of The Meiji Period and the Taisho Period. In the novels of the Meiji Period, female speakers in a sophisticated social group used benefactive verbs to show strong respect and concern for the other person. In the novels of the Taisho Period, female speakers used benefactive verbs to show respect and concern for the other person according to their areas of outside activities. In the novels of the Meiji Period, female speakers used 'Okureru' when the other person was younger than them and was socially and psychologically close to them. Also, 'O~Nasaru' which was one of respect expressions was used by female speakers. Female speakers used it to older people in the Edo period but they also used it to younger people in the Meiji Period. Examples were not shown in the novels of the Taisho Period. Usages of 'Kureru' 'Kudasaru' according to vertical relationships were as follows. If 'a giver' was an older person, 'Kureru' with respect expressions 'Nasaru' 'Nansu' 'Namasu' was used more than 'Kudasaru' in the novels of the Edo Period. However, many examples of 'Kudasaru' were shown on the novels of the Meiji Period and the Taisho period. In the novels of the Meiji Period, 'Okureru' and 'Okurenasaru' which were expressions included in 'Kureru' were shown. Female speakers used them to older people who were socially and psychologically close to them like family. There were not many examples of 'a giver' and 'a receiver' around the same age. However, 'Kureru' and 'Okureru' were used in a younger group and 'Kudasaru' was used in an older group in the novels of the Meiji Period. If 'a giver' was an younger person, 'Kureru' was mainly used in the novels of the Edo period and "Doseishoseikatagi"in Meiji 10s. However, 'Kudasaru' was used many times in the novels of the latter Meiji Period and the Taisho Period.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.423-428
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• 2017

These days, various and tons of education information are rapidly increasing and spreading due to Internet and smart devices usage. Recently, as e-Learning usage increasing, many instructors and students (learners) need to set a goal to maximize learners' result of education and education system efficiency based on big data analytics via online recorded education historical data. In this paper, the author applied Word2Vec algorithm (neural network algorithm) to find similarity among education words and classification by clustering algorithm in order to objectively recognize and analyze online recorded education historical data. When the author applied the Word2Vec algorithm to education words, related-meaning words can be found, classified and get a similar vector values via learning repetition. In addition, through experimental results, the author proved the part of speech (noun, verb, adjective and adverb) have same shortest distance from the centroid by using clustering algorithm.