• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.496-498
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• 2005

본 논문에서는 미등록 어휘로 인한 구문분석의 실패를 해결하는 방법으로 WordNet의 유의어 정보를 이용하였다. 이 방법을 또한 설화용 온톨러지 OfN의 어휘확장에 적용하였다. 실험을 통하여 구문분석 과정에서 나타나는 미등록 어휘문제의 해결과 문장의 의미분석 과정이 순조롭게 진행될 수 있음을 확인하였다.

• Annual Conference of KIPS
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• 2020.11a
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• pp.909-911
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• 2020

소셜 네트워크 서비스 등의 발전으로 인해 개인이 다수에게 의견을 표출하는 통로가 활성화되었다. 게시물에 드러난 감정을 통해 특정 주제에 대한 여론을 도출할 수 있다. 본 논문에서는 BERT를 통한 문장 분석 기술, 그 중에서도 감정 분석을 하는 방법을 분석하고, 이를 일반화된 문장에 적용시키기 위한 데이터 셋 구성에 관하여 연구를 진행하였다.

• Annual Conference of KIPS
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• 2022.11a
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• pp.779-781
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• 2022

본 연구에서는 낚시성 콘텐츠의 주요 구성 요소인 썸네일과 제목을 MLKit와 TF-IDF를 이용하여 분석하고 이를 딥러닝 Sentence BERT 모델에 적용하였다. 이를 활용하여 추후 낚시성 콘텐츠를 걸러내는 알고리즘을 개발 예정이다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.87-97
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• 2019

Reaction of people is importantly considered about specific case as a social network service grows. In the previous research on analysis of social network service, they predicted tendency of interesting topic by giving scores to sentences written by user. Based on previous study we proceeded research of sentiment analysis for social network service's sentences, which predict the result as positive or negative for movie reviews. In this study, we used movie review to get high accuracy. We classify the movie review into positive or negative based on the score for learning. Also, we performed embedding and morpheme analysis on movie review. We could predict learning result as positive or negative with a number 0 and 1 by applying the model based on learning result to social network service. Experimental result show accuracy of about 80% in predicting sentence as positive or negative.

• Smart Media Journal
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• v.9 no.4
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• pp.144-151
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• 2020

In a mobile environment, communication takes place via SMS text messages. Vocabularies used in SMS texts can be expected to use vocabularies of different classes from those used in general Korean literary style sentence. For example, in the case of a typical literary style, the sentence is correctly initiated or terminated and the sentence is well constructed, while SMS text corpus often replaces the component with an omission and a brief representation. To analyze these vocabulary usage characteristics, the existing colloquial style corpus and the literary style corpus are used. The experiment compares and analyzes the vocabulary use characteristics of the colloquial corpus SMS text corpus and the Naver Sentiment Movie Corpus, and the written Korean written corpus. For the comparison and analysis of vocabulary for each corpus, the part of speech tag adjective (VA) was used as a standard, and a distinctive collexeme analysis method was used to measure collostructural strength. As a result, it was confirmed that adjectives related to emotional expression such as'good-','sorry-', and'joy-' were preferred in the SMS text corpus, while adjectives related to evaluation expressions were preferred in the Naver Sentiment Movie Corpus. The word embedding was used to automatically construct a sentiment lexicon based on the extracted adjectives with high collostructural strength, and a total of 343,603 sentiment representations were automatically built.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.2876-2884
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• 2000

Clausal segmentation is the method that parses Korean sentences by segmenting one long sentence into several phrases according to the predicates. So far most of researches could be useful for literary sentences, but long sentences increase complexities of the syntax analysis. Thus this paper proposed Two-Level Clausal Segmentation using sense information which was designed and implemented to solve this problem. Analysis of clausal segmentation and understanding of word senses can reduce syntactic and semantic ambiguity. Clausal segmentation using Sense Information is necessary because there are structural ambiguity of sentences and a frequent abbreviation of auxiliary word in common sentences. Two-Level Clausal Segmentation System(TLCSS) consists of Complement Selection Process(CSP) and Noncomplement Expansion Process(NEP). CSP matches sentence elements to subcategorization dictionary and noun thesaurus. As a result of this step, we can find the complement and subcategorization pattern. Secondly, NEP is the method that uses syntactic property and the others methods for noncomplement increase of growth. As a result of this step, we acquire segmented sentences. We present a technique to estimate the precision of Two-Level Clausal Segmentation System, and shows a result of Clausal Segmentation with 25,000 manually sense tagged corpus constructed by ETRl-KONAN group. An Two-Level Clausal Segmentation System shows clausal segmentation precision of 91.8%.

• Journal of Korean Medical classics
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• v.26 no.4
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• pp.43-70
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• 2013

Objective : analysis of words related with medical concept and context of paragraphs in Suwen of Huang Di Nei Jing based on concreteness and ideality. Method : First, I extract words having medical concept out of the whole sentence of Huang Di Nei Jing. and classifies them according to the type of medical concept. Second, I classify all sentence of it on the basis of analysing of the words. Result : 1. There are not an interrelationship between the abstract and concrete words from the perspective of cause and effect. Abstract words group are divided into two different parts according to the fundamental character. There are the concept being formed from pure idea and the concept being formed from material idea. The main words composed most important part of the oriental medicine have a combination mode with these two word groups. 2. We have several data about concreteness and ideality of Huang Di Nei Jing. Subjects and contents in provisions are concrete relatively. But the logical basis of sentences is remarkably ideational. Many kinds of abstract words are used dominantly to compose logics of these basis. It confirms that concrete words can not be used to make theories and concepts in Suwen. Conclusion : I analyzed words related with medical concept and context of paragraphs in Suwen of Huang Di Nei Jing based on concreteness and ideality and I found that the concept and logical system of Huang Di Nei Jing in the objective point of view.

• Communications of Mathematical Education
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• v.26 no.3
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• pp.301-316
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• 2012

Ball, Thames & Phelps(2008) introduced the idea of Mathematical Knowledge for Teaching(MKT) teacher. Specialized Content Knowledge(SCK) is one of six categories in MKT. SCK is a knowledge base, useful especially for math teachers to analyze errors, evaluate alternative ideas, give mathematical explanations and use mathematical representation. The purpose of this study is to analyze the elementary teacher's SCK. 29 six graders made word problems with respect to division fraction $9/10{\div}2/5$. These word problems were classified four sentence types based on Sinicrope, Mick & Kolb(2002) and then representative four sentence types were given to 10 teachers who have taught six graders. Data analysis was conducted through the teachers' evaluation of the answers(word problems) and revision of students' mathematical errors. This study showed how to know meanings of fraction division for effective teaching. Moreover, it suggested several implications to develop SCK for teaching and learning.

• Journal of Korean Home Economics Education Association
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• v.12 no.3
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• pp.53-63
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• 2000

The purpose of this study was quantitative and qualitative contents analysis of food and nutrition section in middle school textbooks of home economics, physical education and science. As a quantitative approach numbers of sentence lines tables, figures, photos, activities, and exercises were counted. As a qualitative approach, types of explanations were categorized by 7 criteria, and commons and differences of the contents of those subjects were compared. The conclusions of this study were summarized as follows: 1) Contents of food and nutrition section were divided into nutrients. water. energy, food groups, and nutritional problems. When average sentence lines of each were compared, those of nutrients were the longest in all 3 subjects. 2) When compared the numbers of tables, figures, and photos in 3 subjects of textbooks, there were more figures in home economics and science, and more tables in physical education. 3) There were more activities and exercises in home economics an science than in physical education. 4) The D(sentences with table) or E type(sentences with figure) was adapted for the explanation of nutrients functions, recommended dietary allowance, food sources, food groups, eating habits, and weight control in home economics: nutritions functions and energy metabolism in physical education : and digestion, body constituents, energy metabolism, and detection of nutrients in science. 5) Contents about classification and functions of nutrients. food sources deficiency water, energy contents of nutrients and obesity were shown in all 3 subjects. Food groups and eating habits were explained in detail in home economics whereas digestion of nutrients in the digestive tracts were explained in detail in science. Recommended dietary allowance for Koreans and basic food groups revised in 1995 were presented in home economics, whereas those revised in 1989 were presented in physical education. To avoid confusion, recommended dietary allowance for Koreans and food groups presented in physical education tex should be updated.

• Journal of Digital Contents Society
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• v.17 no.3
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• pp.203-210
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• 2016

Korean is free of omission of sentence elements and modifying scope, so managing it on morphological analyzer is better than parser. In this paper, we propose functional expansion methods of the morphological analyzer to ease the burden of parsing. This method is a longest phrase matching method. When the series of several morpheme have one syntax category by processing of Unknown-words, Compound verbs, Compound nouns, Numbers and Symbols, our method combines them into a syntactic unit. And then, it is to treat by giving them a semantic features as syntax unit. The proposed morphological analysis method removes unnecessary morphological ambiguities and deceases results of morphological analysis, so improves accuracy of tagger and parser. By empirical results, we found that our method deceases 73.4% of Parsing tree and 52.4% of parsing time on average.