• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.149-156
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• 2021

Text mining is one of the useful tools to discover public opinion and perception regarding political issues from big data. It is very common that users of social media express their opinion with newly-coined words such as slang and emoji. However, those new words are not effectively captured by traditional text mining methods that process text data using a language dictionary. In this study, we propose effective methods to extract newly-coined words that connote the political stance and opinion of users. With various text mining techniques, I attempt to discover the context and the political meaning of the new words.

• Proceedings of the Korean Society for Language and Information Conference
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• 2002.06a
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• pp.10-38
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• 2002

본고는 한국어 영형 대명사의 적절한 해석을 위해 개념적으로 전혀 새로운 이론을 제안한다. 일련의 다양한 제약들이 서로 연관되어 있음을 보인 후, 그러한 규칙의 다양성을 적절히 포착하기 위해 적절성 이론 (Optimality Theory)을 도입할 것을 제안하고, 그 토대 위에 다양한 제약들을 형식화한 후, 그 규칙들의 위계관계를 설정한다. 가장 우선순위를 갖는 제약으로 인접 요소간 어휘의미자질들이 일치해야 한다는 어휘의미제약(*Feature Mismatch)과 통사적 결속규칙을 의미론적으로 재해석한 결속원리 B(Principle B)를 선정한다. 그 다음 순위를 갖는 제약으로, 가능한 한 선행명사를 지칭하도록 요구하는 대용존중제약(DOAP: Don't Overlook Anaphoric Possibilities)과, 센터링 이론의 전이방식 개념을 도입하여 정의한 계속선호제약 (CONTINUE)을 제안한다

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.426-435
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• 2021

This study aims to functionally classify lexical bundles appearing in academic papers on nuclear science and engineering written in English and then analyze the lexical bundles' characteristics compared to those appearing in general academic papers. To this end, the texts of nuclear science and engineering papers were collected and produced as a corpus(c. 1 mil. tokens). Then they were statistically compared through Chi-square tests and standardized residuals with the corpus of general academic papers(c. 750,000 tokens). The results revealed that, compared to general academic papers, the bundles in the stance lexical bundle category were mainly used among the functional lexical bundle in nuclear science and engineering. The use of the lexical bundles lacked much variety. The same type of lexical bundles was 're-used' and 'recycled'. Based on these research results, educational implications for English for Academic Purposes and the further direction of follow-up research were discussed and suggested.

• The Journal of the Korea Contents Association
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• v.17 no.6
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• pp.408-414
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• 2017

This paper aims to investigate the value and resilience of Nogeoldae which was written at the end of Koryo dynasty and has been used as the most important foreign language education materials throughout the 500 years of Chosun dynasty. To this end, 106 volumes of dialogues, 12 of meeting, 17 of lodging, 21 of Daedo bound, 34 of Daedo lives and 11 of return in Nogeoldae are analyzed by an average length of the sentences, an average length of words, type-token ratio, number of words before main verbs and number of words before nouns to identify the progressive degree of the complexity. The result of the analysis shows that Nogeoldae presents a desired progressive complexity found in modern foreign language textbooks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.8
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• pp.5565-5570
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• 2015

Implicit citation sentence recognition is to locate citation sentences which lacks explicit citation markers, from articles' full-text. State-of-the-art approaches exploit word ngrams, clue words, researcher's surnames, mentions of previous methods, and distance relative to nearest explicit citation sentences, etc., reaching over 50% performance. However, most previous works have been conducted on English. As for Korean, a rule-based method using positive/negative clue patterns was reported to attain the performance of 42%, requiring further improvement. This study attempted to learn to recognize implicit citation sentences from Korean literatures' full-text using Korean lexical features. Different lexical feature units such as Eojeol, morpheme, and Eumjeol were evaluated to determine proper lexical features for Korean implicit citation sentence recognition. In addition, lexical features were combined with the position features representing backward/forward proximities to explicit citation sentences, improving the performance up to over 50%.

• Annual Conference on Human and Language Technology
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• 2004.10d
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• pp.118-125
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• 2004

인간의 언어지식은 다양한 개념 관계를 가지며 서로 망(network)의 모습으로 연결되어 있다. 인간의 언어지식의 산물 중에서 가장 체계적이며 구조적으로 언어의 모습을 드러내고 있는 결과물이 사전이라고 할 수 있다. 본 논문에서는 이러한 사전 뜻풀이 말에서 개념 어휘와 자동적인 지식획득을 통하여 의미 정보를 구조적으로 추출한다. 이러한 의미 정보가 추출되면서 동시에 자동적으로 개념 어휘의 의미 참조 모형이 구축된다. 이러한 것은 사전이 표제어 리스트와 표제어를 기술하는 뜻풀이말로 이루어진 구조의 특성상 가능하다. 먼저 172,000여 개의 사전 뜻풀이말을 대상으로 품사 태그와 의미 태그가 부여된 코퍼스에서 의미 정보를 추출하는데, 의미분별이 처리 된 결과물을 대상으로 하기 때문에 의미 중의성은 고려하지 않아도 된다. 추출된 의미 정보를 대상으로 정제 작업을 거쳐 정보이론의 상호 정보량(Ml)을 이용하여 개념 어휘와 의미 정보간에 연관도를 측정한 후, 개념 어휘간의 유사도(SMC)를 구하여 지식표현의 하나로 연관망을 구축한다.

• Korean Journal of Oriental Medicine
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• v.12 no.3 s.18
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• pp.1-15
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• 2006

For the recent several years, there has been much interest in lexical semantic network. However, it seems to be very difficult to evaluate the effectiveness and correctness of it and invent the methods for applying it into various problem domains. In order to offer the fundamental ideas about how to evaluate and utilize lexical semantic networks, we developed two automatic word clustering systems, which are called system A and system B respectively. 68,455,856 words were used to learn both systems. We compared the clustering results of system A to those of system B which is extended by the lexical-semantic network. The system B is extended by reconstructing the feature vectors which are used the elements of the lexical-semantic network of 3,656 '-ha' verbs. The target data is the 'multilingual Word Net-CoreNet'.When we compared the accuracy of the system A and system B, we found that system B showed the accuracy of 46.6% which is better than that of system A, 45.3%.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.67-74
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• 2020

In this paper, we proposes to develop an online academic vocabulary dictionary as a way of educating academic vocabulary for content-oriented Korean language education. Various academic languages exist in the content-based Korean language teaching materials they encounter when studying at university. You cannot understand or produce academic text without knowing the academic vocabulary. Therefore, one of the tasks of Korean language education has become to improve educational efficiency by preparing a method for academic vocabulary education that is most suitable for them in consideration of their own. Prior to the development of the online academic vocabulary dictionary, the institute conducted a basic study on how the content should be contained in the online dictionary. Online academic vocabulary dictionaries allow students to naturally link their limited education into and out of the classroom, thereby overcoming the limitations of vocabulary education at the educational scene and maximizing their educational effectiveness.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.315-317
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• 2012

지식iN 등의 지식검색 서비스는 잘못된 답변으로 인한 낮은 신뢰성과 다수의 중복 답변 등의 문제점을 가진다. 질의문 '세상에서 가장 큰 나라'에 대해서 관련된 모든 질문과 답변을 제시하지 않고 질의문과 관련된 다수의 답변을 분석하여 답변 '러시아'를 추천하여 제시할 수 있다면 지식검색의 효용성과 신뢰성이 크게 향상될 수 있다. 본 논문에서는 질문-답변의 유형을 단어, 글, 도표, 목록의 네가지로 분류하고, 그 중 단어 유형에 대한 답변 추천 방법을 제시한다. 질의문에 대해 검색된 질문을 군집화하고, 질문에 대한 답변들에 대해서 TF, IDF, 어휘간 거리 정보를 다양하게 결합하여 어휘의 점수를 계산한다. 각 군집에서 가장 높은 점수를 가지는 어휘를 해당 군집에서 가장 중요한 어휘로 보고 추천 정답으로 제시한다. 단어 유형인 질문 100개에 대한 네이버 지식iN에 대한 시스템 평가에서 추천된 상위 1위에 대해서는 68%의 정답률을, 상위 5위까지에 대해서는 89%의 정답률을 보였다.

• The Journal of the Korea Contents Association
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• v.17 no.10
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• pp.85-93
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• 2017

Standardization of disaster safety lexicon is important as the most basic process for successful accident prevention and response. A lack of understanding of disaster safety lexicon leads lack of communication and information sharing, which can be a problem in communicating with appropriate responses in case of a disaster. Currently disaster and safety control agencies produce and manage heterogeneous information and they also develop and use word dictionaries individually. To solve this problem, identifying differences of disaster safety lexicon patterns by the user are essential for standardization. In this paper, we conducted lexicon patterns analysis based on social media and revealed the characteristics according to pattern types. At the result, we proposed the standardization and construction methods of disaster safety word dictionary.