• Journal of the Korean earth science society
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• v.27 no.1
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• pp.20-31
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• 2006

This study analysed student teachers' notable questions about the earth science contents in the elementary science textbooks. The contents of notable questions were defined as ‘notable question contents 1' and 'notable question contents 2'. Both the question contorts are contents about which the number of questions is above three times and from two times to three times as much as the mean number of questions per page of each unit respectively. The results are as follows. First, question contents 1 are found as 'clouds observation', 'geological strata formation' and so on. Question contents 2, 'rainfall measurement', 'moon's movement during one night' and so on are found. Second, the number of interrelationships of main terms in questions increased in each question of question contents 1, but 4 term-patterns are found more in question contents 2 than question contents 1. Third, high interrelationship patterns of terms in question contents 1 are 'coal and petroleum-generation', 'metamorphosis-heat and pressure', 'metamorphosis-heat and pressure-metamorphic rocks', 'planet-sun-comet-revolution' and in question contents 2. 'constellation plate-use', 'dryness and wetness hygrometer-principle', 'seismograph-principle-earthquake', 'earth rotation axis-tilting-occurrence', 'dryness and wetness hygrometer-principle-humidity' and so on. The sources of questions analysed in this study are estimated as the content construction system of textbooks, or students' general questions about the earth science contents. If this is the former, the problems in texts and illustrations in textbooks should be articulated and resolved. And if the latter, the elementary science curriculum has to be reconsidered in view of scientific literacy in earth science.

• Journal of KIISE:Software and Applications
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• v.33 no.7
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• pp.636-645
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• 2006

A tenn with more domain specific information has higher level of term specificity. We propose new specificity calculation methods of terms based on information theoretic measures using compositional and contextual information. Specificity of terms is a kind of necessary conditions in tenn hierarchy construction task. The methods use based on compositional and contextual information of terms. The compositional information includes frequency, $tf{\cdot}idf$, bigram and internal structure of the terms. The contextual information of a tenn includes the probabilistic distribution of modifiers of terms. The proposed methods can be applied to other domains without extra procedures. Experiments showed very promising result with the precision of 82.0% when applied to the terms in MeSH thesaurus.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.891-897
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• 2022

In big data analysis, raw text data mostly exists in various unstructured data forms, so it becomes a structured data form that can be analyzed only after undergoing heuristic pre-processing and computer post-processing cleansing. Therefore, in this study, unnecessary elements are purified through pre-processing of the collected raw data in order to apply the wordcloud of R program, which is one of the text data analysis techniques, and stopwords are removed in the post-processing process. Then, a case study of wordcloud analysis was conducted, which calculates the frequency of occurrence of words and expresses words with high frequency as key issues. In this study, to improve the problems of the "nested stopword source code" method, which is the existing stopword processing method, using the word cloud technique of R, we propose the use of "general stopword corpus" and "user-defined stopword corpus" and conduct case analysis. The advantages and disadvantages of the proposed "unstructured data cleansing process model" are comparatively verified and presented, and the practical application of word cloud visualization analysis using the "proposed external corpus cleansing technique" is presented.

• Journal of Scientific & Technological Knowledge Infrastructure
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• s.17
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• pp.66-67
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• 2005

• The Science & Technology
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• v.34 no.3 s.382
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• pp.10-12
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• 2001

• The Science & Technology
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• v.26 no.1 s.284
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• pp.102-105
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• 1993

• The Science & Technology
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• v.9 no.7 s.86
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• pp.6-7
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• 1976

• The Science & Technology
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• v.35 no.7 s.398
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• pp.40-42
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• 2002

• The Science & Technology
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• v.26 no.10 s.293
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• pp.86-89
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• 1993

• Journal of Scientific & Technological Knowledge Infrastructure
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• s.22
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• pp.46-47
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• 2006