• Journal of Korean Elementary Science Education
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• v.35 no.1
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• pp.1-12
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• 2016

The purpose of this study was to analyze question's patterns in elementary school science teacher's guide books of 5, 6th grade under the 2009 revised curriculum. A modified analysis framework based on Blosser's classified system was used to analyze 1,982 questions extracted from elementary science teacher's guide books by grade, by domain, and by teaching and learning stage. The findings of this study were as follows. First, of the 1,982 questions, the most prominent type of question was the propositional question and the following was the reproductive question. And, in comparing the question's patterns between 5, 6th grade, it was found that 6th grade had higher rate of close typed question, while 5th grade had higher rate of open typed question in its curriculum. Secondly, a comparative study about two domains, material and energy science domain and earth and life science domain, showed that the number of questions of each domain was not much different. However, it was found that propositional questions and applicable questions showed a higher rate in material and energy science domain, and anticipated questions and open typed questions including divergent and evaluative question showed higher rate in earth and life science domain. Moreover, although the total number of questions from integration and my fun research domain's contents was small, the rate of open typed questions was higher than any other domains. Finally, as a result of comparing and analyzing question's pattern in teaching and learning stages, the rate of reproductive question and anticipated questions was high at the stage of introduction. At the stage of development, the rate of propositional and reproductive questions was high. At the stage of conclusion, the rate of synthetic and applicable questions was high.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.575-590
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• 2000

In this paper, we extended the Kim et al.'s two-stage unrelated question model(1992) and the Lee et al.'s improved unrelated question model(1998) to two sample unrelated question model of using two independent samples in the case of unknown $\pi_y$.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.6
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• pp.539-545
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• 2004

For question answering system that extracts an answer and output to user‘s natural language question, a process of question type classification from user’s natural language query is very important. This paper proposes a question and answer type classifier using the interrogatives and word semantic categories instead of complicated classifying rules and huge dictionaries. Synonyms and postfix information are also used for question type classification. Experiments show that the semantic categories are helpful for question type classifying without interrogatives.

• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.825-835
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• 2002

In this paper, we extend the conditional unrelated question model which was suggested by Lee and Hong(2000) to two-sample case when there is no information about the true proportion of the unrelated character Y. Conditions are obtained under which the proposed model is more efficient than Carr et al.\`s conditional modal and Greenberg et al.'s two-sample unrelated question model.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.753-765
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• 2001

We suggest a quantitative conditional unrelated question model that can be used in obtaining more sensitive information. For whom say "yes" about the less 7han sensitive question .B we ask only about the more sensitive variable X. We extend our model to two sample case when there is no information about the true mean of the unrelated variable Y. Finally we compare the efficiency of our model with that of Greenberg et al.′s.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.73-83
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• 2011

The question answering system shows the answers that are input by other users for user's question. In spite of many researches to try to enhance the satisfaction level of answers for user question, there is a essential limitation. So, the question answering system provides users with the method of recommendation of another questions that can satisfy user's intention with high probability as an auxiliary function. The method using the fuzzy relational product operator was proposed for recommending the questions that can includes largely the contents of the user's question. The fuzzy relational product operator is composed of the Kleene-Dienes operator to measure the implication degree by contents between two questions. However, Kleene-Dienes operator is not fit to be the right operator for finding a question answers pair that semantically includes a user question, because it was not designed for the purpose of finding the degree of semantic inclusion between two documents. We present a novel fuzzy implication operator that is designed for the purpose of finding question answer pairs by considering implication relation. The new operator calculates a degree that the question semantically implies the other question. We show the experimental results that the probability that users are satisfied with the searched results is increased when the proposed operator is used for recommending of question answering system.

• Journal of Korean Library and Information Science Society
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• v.36 no.4
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• pp.329-347
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• 2005

Collaborative Digital Reference Service( CDRS) has spread worldwide as a tool of obtaining high-quality information in knowledge information society KISTI formed CDRS collaboration model called Question포인트+ with 4 institutions in Korea. In Question포인트+ which is also linked to the Global Network, the domestic and oversea information specialists answer a variety of questions in response of the requests of users on the web. The implementation of Question포인트+ is significant as the first domestic CDRS collaboration model, and contribute to management, dissemination, and activation of CDRS in Korea.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.476-482
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• 2004

Question-Answering (QA) system has to analyze user's intention correctly to respond correct answer for user's question., This paper proposes a focus-word-based question analysis approach for Korean QA system to analyze user's intention correctly. focus-word is a clue-word which selects question type. The question type is determined to one in 75 subcategories using semantics of focus-words. the proposed system accomplished 97.18% accuracy for the main category and 95.31% accuracy for the subcategory in the question classification.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.1
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• pp.483-511
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• 2015

The efficiency and quality of the social reference sites are being challenged because a large quantity of the questions have not been answered or satisfied for quite a long time. Main goal of this study is to investigate important factors that affect the performance of question triage to relevant answerers in the context of social reference. To achieve the goal, expert finding techniques were used to construct an automated question triage approach to resolve this problem. Furthermore, important factors affecting triage decisions in digital reference were first examined, and extended them to the social reference setting by investigating important factors affecting the performance of automated question triage in the social reference setting. The study was conducted using question-answer pairs collected from Ask Metafilter. For the evaluation, logistic regression analyses were conducted to examine which factors would significantly affect the performance of predicting relevant answerers to questions. The results of the current study have important implications for research and practice in automated question triage for social reference. Furthermore, the results will offer insights into designing user-participatory digital reference systems.

• Journal of Digital Contents Society
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• v.19 no.5
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• pp.927-933
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• 2018

One of the factors that have the greatest influence on the error of the question answering system that finds and provides answers to natural language questions is the step of searching for documents or passages that contain correct answers. In order to improve the retrieval performance, it is necessary to understand the characteristics of documents and passages containing correct answers. This paper experimentally analyzes how many question words appear in the correct answer documents, how the location of the question word is distributed, and how the topic of the question and the correct answer document are similar using the corpus composed of the question, the documents with correct answer, and the documents without correct answer. This study explains the causes of previous search research results for question answer system and discusses the necessary elements of effective search step.