• Journal of Industrial Convergence
• /
• v.21 no.1
• /
• pp.187-192
• /
• 2023

In this study, trends in ICT education were investigated by analyzing the frequency of appearance of keywords related to machine learning and using conversion of iteration correction(CONCOR) techniques. A total of 304 papers from 2018 to the present published in registered sites were searched on Google Scalar using "ICT education" as the keyword, and 60 papers pertaining to ICT education were selected based on a systematic literature review. Subsequently, keywords were extracted based on the title and summary of the paper. For word frequency and indicator data, 49 keywords with high appearance frequency were extracted by analyzing frequency, via the term frequency-inverse document frequency technique in natural language processing, and words with simultaneous appearance frequency. The relationship degree was verified by analyzing the connection structure and centrality of the connection degree between words, and a cluster composed of words with similarity was derived via CONCOR analysis. First, "education," "research," "result," "utilization," and "analysis" were analyzed as main keywords. Second, by analyzing an N-GRAM network graph with "education" as the keyword, "curriculum" and "utilization" were shown to exhibit the highest correlation level. Third, by conducting a cluster analysis with "education" as the keyword, five groups were formed: "curriculum," "programming," "student," "improvement," and "information." These results indicate that practical research necessary for ICT education can be conducted by analyzing ICT education trends and identifying trends.

• Proceedings of the Korean Society for Cognitive Science Conference
• /
• 2006.06a
• /
• pp.33-37
• /
• 2006

본 논문은 어휘판단과제(LDT: Lexical Decision Task)시 나타나는 여러 언어현상 중 단어빈도효과(word frequency effect)와 단어유사성효과(word similarity effect)를 한국어에 적용시켜 인간과 계산주의적 모델을 통해 실험하고, 결과를 비교하였다. 실험결과 인간과 계산주의적 모델 각각 한국어에 대해 단어빈도효과와 단어 유사성효과를 보였으며, 인간의 실험결과와 계산주의적 모델의 결과가 유의미한 유사성을 나타내었다.

• Proceedings of the KAIS Fall Conference
• /
• 2006.05a
• /
• pp.391-393
• /
• 2006

본 논문은 어휘판단과제(LDT: Lexical Decision Task)시 나타나는 여러 언어현상 중 단어빈도효과(word frequency effect)와 단어유사성효과(word similarity effect)를 한국어에 적용시켜 인간과 계산 주의적 모델을 통해 실험하고, 결과를 비교하였다. 실험결과 인간과 계산주의적 모델 각각 한국어에 대해 단어빈도효과와 단어 유사성효과를 보였으며, 인간의 실험결과와 계산주의적 모델의 결과가 유의미한 유사성을 나타내었다.

• The KIPS Transactions:PartB
• /
• v.19B no.1
• /
• pp.37-42
• /
• 2012

The pseudo relevance feedback suggests that frequent words at the top documents are related to initial query. However, the main drawback associated with the term frequency method is the fact that it relies on feature independence, and disregards any dependencies that may exist between words in the text. In this paper, we propose query expansion based on word graph using term proximity. It supplements term frequency method. On TREC WT10g test collection, experimental results in MAP(Mean Average Precision) show that the proposed method achieved 6.4% improvement over language model.

• The Journal of the Korea Contents Association
• /
• v.10 no.4
• /
• pp.433-437
• /
• 2010

Patent contains meaningful technical achievement. There are many cases explaining technology trends from the analysis of frequency of term. Term sometimes has different meaning on fields. In this paper, words from patent titles of US, Japan, Korea PCT and EPO are collected by the 5 categories of WIPO. Frequency changes rate of each word were calculated and high ranked words of 5 categories were analyzed to find relationship between patent and technology development as well as technology trends.

• Journal of Korean Home Economics Education Association
• /
• v.31 no.3
• /
• pp.73-90
• /
• 2019

The purpose of this study was to explore the differences in perceptions of male and female students about population problems and to draw up implications for population education. Using text mining, the report about population problem, which had written by students in population education class, were analysed. After extracting key words, semantic networks were visualized. The results were as follows. First, the high frequency words were the same for each gender. Second, key words based on frequency did not differ depending on gender. And the key words extracted by the correlation analysis and bigram were different. That is, in the semantic network of girls' words, the network of "life"-"marriage"-"birth"-"pregnancy" appeared independently, distinguishing it from male students who showed separate objective links to population problems. Therefore, it drew suggestions that male and female students should be viewed as heterogeneous groups with different cognitive structures on population problems and that the content and methods of population education should be approached differently depending on gender.

• Journal of Internet Computing and Services
• /
• v.12 no.1
• /
• pp.111-118
• /
• 2011

While sharing information through Internet by Web page or XML, familiar labels of information items will reduce the confusion among users. The advises of the language experts for choosing familiar terms may cost money and time, but an automated Indicator can help a user select right terms without any cost. This paper collects Indicators that can be easily found over Internet and compares the efficiency of them for selecting familiar terms. The collected indicators are the number of words in a term, the frequency used in a related Web sites, and the number of search results in portal sites. The results conclude that the found terms by the frequency matches 76% for women's and 71% for men's, which tells that the frequency can be a reference for selecting familiar terms.

• Proceedings of the Korean Society for Information Management Conference
• /
• 2001.08a
• /
• pp.181-184
• /
• 2001

본 논문에서는 적합한 색인어를 자동으로 추출해 내기 위해 잘 알려진 통계적 기법과 구문분석적 기법을 혼용하였다. 적용결과를 검색효율로 나타내지 않고 각 방법에 따라 추출된 단어들을 실증적으로 보여주어 성능에 대한 판단을 유도하였다. 빈도나 품사가 단독으로 사용된 것보다 동시에 적용된 것이 보다 좋은 결과를 가져왔다.

• The KIPS Transactions:PartB
• /
• v.8B no.3
• /
• pp.251-260
• /
• 2001

기존의 베이지안 문서 분류는 문서의 특징 표현에 있어서 단어간의 의미를 정확하게 반영하지 못하는 문제점이 있다. 이러한 문제점을 해결하기 위해, 본 논문에서는 Apriori-Genetic 알고리즘을 이용한 베이지안 문서 분류 방법을 제안한다. Apriori 알고리즘은 단어간의 의미를 반영한 연관 단어의 형태로 문서의 특징을 추출하며 추출된 연관 단어로 연관 단어 지식베이스를 구축한다. Aprrori 알고리즘만으로 연관 단어 지식베이스를 구축할 경우, 지식베이스 안에 부적당한 연관 단어가 포함된다. 따라서 문서 분류의 정확도가 낮아지는 단점이 있다. 이러한 단점을 보완하기 위해, Genetic 알고리즘을 이용하여 연관 단어 지식베이스를 최적화하는 방법을 사용한다. 베이지안 확률을 이용하는 분류자는 최적화된 연관 단어 지식베이스를 기반으로 문서를 클래스별로 분류한다. Apriori-Genetic 알고리즘을 이용한 베이지안 문서 분류의 성능을 평가하기 위해, Apriori 알고리즘을 이용한 베이지안 문서 분류 방법, 역문헌빈도를 사용한 베이지안 문서 분류 방법, 기존의 단순 베이지안 분류 방법과 비교하였다.

• Proceedings of the Korean Society for Cognitive Science Conference
• /
• 2000.06a
• /
• pp.169-175
• /
• 2000

말모둠은 특성에 따라서 여러 성격을 나타내게 된다. 하지만 말모둠의 특성을 자동적으로 알아내는 방법은 간단하지 않다. 중요 단어를 가지고 있으면 말모둠에서 통계적으로 많은 부분에서 적용시켜 말모둠의 특성을 파악할 수 있다. 본 논문에서는 한국어 말모둠에서 나타나는 동사류 단어들의 빈도를 분석한다. 또한, 사람이 직접 중요도를 평가한 사전의 단어들과 말모둠에 나타나는 단어들을 비교해서 통계적 차이점을 알아보고, 그 차이점을 통해 앞으로 연구할 일에 대해서 토론한다. 간단한 실험을 통해 사람의 평가한 중요도 점수의 효용성도 알아본다.