• Annual Conference on Human and Language Technology
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• 2008.10a
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• pp.35-41
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• 2008

이 논문은 각 문법 관계(grammatical relation)에서 선호되는 체언 어휘를 파악하고, 이 어휘들의 의미적 유형 및 그 위계를 파악하는 것이 목적이다. 이를 위해 80만 어절의 21세기 세종계획 구문분석 말뭉치에서 그 분포를 추출하고, 통계적 검증을 통해 각 문법 관계에서 선호되는 체언 어휘를 선별한다. 이 연구에서 관찰하는 문법 관계는 주어, 목적어, 용언수식어로 하며, 이들 문법 관계에서 선호되는 어휘 추출 대상 품사는 대명사, 고유명사, 일반명사로 한다. 한정성의 강도에 따라 주어 분포 경향이 나타나며, 이에 따라 대명사 > 고유명사 > 일반명사 순으로 주어 분포 경향이 나타난다. 그러나 일반적 예측과 다르게 한정성의 강도가 더 강한 것으로 알려진 대명사가 고유명사보다 목적어와 용언수식어에서 분포 경향이 더 강하여, 일반명사 > 대명사 > 고유명사의 순으로 분포 경향이 나타난다. 대명사, 고유명사, 일반명사는 공통적으로 주어에서는 사람 지시어, 목적어에서는 사물과 장소 지시어, 그리고 용언수식어에서는 시공간 표현이 선호되어 분포한다. 특히 대명사는 각 문법기능에서 인칭대명사의 경우 인칭에 따라, 그리고 지시대명사의 경우 원근칭에 따라 선호도의 차이를 보인다. 이러한 체언 어휘의 의미적 분포 특성은 문법 관계에 통사적 기능 외에도 의미적 경향이 반영된 것으로 고려될 수 있다.

• Communications of Mathematical Education
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• v.37 no.4
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• pp.675-695
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• 2023

To learn mathematics effectively, understanding vocabulary is essential. Accordingly, as a way to present vocabulary for mathematics education, high-frequency vocabulary was extracted from the 2009 revised 1st and 2nd grade mathematics textbooks and the 2015 revised 1st and 2nd grade mathematics textbooks. At this time, mathematics textbooks were analyzed by grade and semester, and vocabulary with a common frequency of 5 or more was extracted. In order to use it effectively in school settings, common vocabulary for each grade and intensive vocabulary for each semester were presented. As a result of the study, 61 vocabulary words for first grade education and 121 vocabulary words for second grade education were selected. As a result of analysis by vocabulary level, various levels of vocabulary from grades 1 to 5 were used. As a result of analysis by vocabulary type, the proportion of academic words increased similarly, but the proportion of technical words was found to be highest in the first semester of the second year. Based on these results, the extracted vocabulary for mathematics education is used as a resource for vocabulary instruction for students' mathematics education in each grade to help students learn mathematics.

• The Journal of the Korea Contents Association
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• v.17 no.12
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• pp.378-385
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• 2017

The aim of this study was to analyze cohesion and word information among different types of questions in the English reading section of the College Scholastic Ability Tests (CSAT). The types of questions were divided into three categories: macro reading, micro reading, and indirect writing. Reading texts from 1994 to 2017 CSAT were analyzed by Coh-Metrix, an automated evaluation program of text and discourse. The findings of this study indicated that there were statistical differences among the three categories of questions for noun overlap, stem overlap, adversative and contrastive connective, additive connective, pronoun incidence, age of acquisition, concreteness for content word, imagability, and meaningfulness. The information of the findings bore pedagogic implications for developing textbooks, questions for CSAT, and reading strategies by students.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.195-197
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• 1998

복합명사 분석은 조합이 자유롭고 제한이 없으므로 여러 가지 모호성을 발생시킨다. 이러한 모호성을 해결하는 기존 방법으로 사전을 이용하는 방법[2]과 통계적 정보를 이용하는 방법[3,4]이 있다. 본 논문에서는 하위 범주화된 어휘 정보를 가진 전자사전을 이용하여 복합명사를 분석한다. 그리고 어휘 정보만으로 처리했을 때 의미상으로 잘못된 분석이 발생할 수 있으므로 본 논문은 복합명사를 구성하는 어휘의 정보와 특정단어의 의미에 따른 복합명사 제약조건을 규칙베이스로 구축하여 분석에 이용한다. 또한 분석에 실패한 복합명사의 유형을 분석하여 각 유형에 따른 교정 방법도 제시한다. 실험 데이터는 부산일보, 교과서, 그리고 각종 문서에서 무작위로 추출한 27,945개의 복합명사를 사용하였다. 본 논문에서 제시한 의미적 제약조건을 이용하여 분석했을 때 복합명사로 잘못 쓴 어절의 검사율이 21% 향상되었다.

• Annual Conference on Human and Language Technology
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• 2015.10a
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• pp.41-45
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• 2015

무제한 정보추출은 주로 영어를 대상으로 연구가 진행 되었지만, 최근에는 영어가 아닌 다른 언어에 대한 적용이 시도되고 있다. 본 논문에서는 관계 어휘의 유형을 동사형과 명사형 2가지로 정의하고, 각 유형별로 구문 분석 결과 기반의 서로 다른 방법론을 적용하는 한국어 대상 무제한 정보추출 시스템을 소개한다. 동사형 관계 어휘에 대해서는 의존 관계 기반의 추출 규칙을 적용하고, 명사형 관계 어휘에 대해서는 대량의 말뭉치로부터 자동으로 학습한 의존 관계 구조 기반의 추출 패턴을 적용한다. 임의의 100개 문장에 대해서 수행한 결과는 산출된 전체 트리플에 대해 0.8이상의 정밀도를 보임으로써 본 논문에서 제안하는 방법의 효용성을 증명하였다.

• Proceedings of the Korean Society for Information Management Conference
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• 2001.08a
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• pp.29-34
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• 2001

영상자료 및 소리자료의 색인과 검색을 위해서는 감정동사 및 감정형용사 등의 감정 어휘를 필요로 한다. 그러나 감정어휘는 그 뉘앙스가 미묘하여 분명한 분류체계가 없이는 체계적인 정리가 불가능하다. 이에 따라 본 연구에서는 국어학과 분류사전의 분류체계를 고찰하고 새로운 감정어휘의 분류방안을 연구하였으며, 감정에 따른 기쁨, 슬픔, 놀람, 공포, 혐오, 분노의 6가지 기본유형을 제시하였다.

• Science of Emotion and Sensibility
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• v.12 no.2
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• pp.215-224
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• 2009

This research extracted emotional words for measuring user's emotions expressed while using a cellular phone and a web. And then the emotional words were compared to find out whether the difference in emotional words by the type of products. The results of this study suggest that the hardware-oriented products used for specific purpose such as cellular phone extracted a lot of emotional words related to 'Satisfaction in Usability' and 'Pleasure'. 'Satisfaction in Usability' are conceived satisfying in usability or practicality of product. 'Pleasure' are pleasant emotions expressed while using a product. However the emotional words related to 'Aesthetics' and 'Novelty' were omitted. 'Aesthetics' are expressed by product's appearance and by various visual information while using a product. 'Novelty' are expressed by something that is novel and new that has never been experienced. On the other hand the software-oriented products used rather to find something better and new information than to perform specific tasks such as web extracted a lot of emotional words related to 'Novelty'. Therefore, the results of this research have found evidence that it is desirable to make a set of subjective evaluation scale by the type of products. When making the subjective evaluation scale, it is important to use appropriate emotional words for the purpose of use and the characteristics of those products.

• Journal of Korean language education
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• v.28 no.4
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• pp.245-271
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• 2017

This study aimed at investigating the lexical diversity in the "Frog Story" narratives of Korean-Chinese bilingual children. Six bilingual speakers of Korean children- four boys and two girls- were audio recorded as they produced narratives based on pictures from the Mercer Mayer book "Frog, where are you?" The order of narration was counterbalanced. The vocabularies from narratives were analyzed by type, token, TTR (type-token Ratio) and D value using the CLAN (Computerized Language Analysis) program. The findings showed that the pattern of lexical diversity in Korean is similar with the Chinese, but the TTR and D value of Chinese still remain low in comparison with those of Korean. In addition, Korean language seems to have significant influence on Chinese in the language usage pattern and vice versa.

• Journal of the Korea Convergence Society
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• v.11 no.1
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• pp.201-209
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• 2020

The purpose of this study is to investigate the effect of narrative and expository texts on incidental vocabulary learning of Korean university EFL learners while reading. The experimental groups were divided into 3 groups. The first experimental group were exposed to narrative texts and second group received expository texts. And the third group were administered narrative and expository texts alternately. The vocabulary tests were conducted after the last session to assess the incidental vocabulary gains of the learners. The results indicated the superiority of the expository texts over narratives in terms of enhancing learners' incidental acquisition of unknown words. Moreover, the results showed that the blended reading group of expository and narrative texts did better on the vocabulary gains than those of narrative reading group and expository reading group. However, in the essay writing assessment, the expository group committed the most vocabulary errors in writing.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.541-548
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• 2016

The use of social media is already essential as a source of information for listening user's various opinions and monitoring. We define social 'risks' that issues effect negative influences for public opinion in social media. This paper aims to discriminate various linguistic features and reveal their effects for building an automatic classification model of social risks. Expecially we adopt a word embedding technique for representation of linguistic clues in risk sentences. As a preliminary experiment to analyze characteristics of individual features, we revise errors in automatic linguistic analysis. At the result, the most important feature is NE (Named Entity) information and the best condition is when combine basic linguistic features. word embedding, and word clusters within core predicates. Experimental results under the real situation in social bigdata - including linguistic analysis errors - show 92.08% and 85.84% in precision respectively for frequent risk categories set and full test set.