• Korean Journal of Cognitive Science
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• v.17 no.4
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• pp.271-286
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• 2006

In this paper, we introduce automatic question generation technique using the language resources like User-Word Intelligent Network(U-WIN) and Korean dictionary including quite a for of information. And we present Korean word learning system with this technique. The item pool method which almost learning-system are using makes some problems. As a solution of the problems, we classified into 8 question type and implemented the Korean word learning system which is making the Korean question automatically by using the morphological and semantic information according to the automatic question generation pattern of each type.

• Journal of Korean Elementary Science Education
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• v.33 no.4
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• pp.700-709
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• 2014

The purpose of this study was to examine the effects of question-generating strategy on science academic achievement, scientific attitude in elementary science class. To examine the effects of question-generating strategy this learning materials were applied to elementary science curriculum, and an experimental group and a control group were selected from $5^{th}$ graders at H elementary school located in Gyeonggi-do. Students were taught for 6 weeks. Control group take traditional lessons and solve questions presented textbook. Question generated group generate questions, solve them and feed back by themselves. The results of this study were found statistically significant difference in the pupil's enhancement of the science academic achievement, scientific attitude (p<.05). Thus question-generating strategy for elementary science inquiry instruction that has a positive effect on interests in class is useful and better be widely applied to science education.

• Journal of KIISE:Databases
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• v.32 no.3
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• pp.297-303
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• 2005

When confronted with a query, question answering systems endeavor to extract the most exact answers possible by determining the answer type that fits with the key terms used in the query. However, the efficacy of such systems is limited by the fact that the terms used in a query may be in a syntactic form different to that of the same words in a document. In this paper, we present an efficient semantic query expansion methodology based on a question category concept list comprised of terms that are semantically close to terms used in a query. The semantically close terms of a term in a query may be hypernyms, synonyms, or terms in a different syntactic category. The proposed system constructs a concept list for each question type and then builds the concept list for each question category using a learning algorithm. In the question answering experiments on 42,654 Wall Street Journal documents of the TREC collection, the traditional system showed in 0.223 in MRR and the proposed system showed 0.50 superior to the traditional question answering system. The results of the present experiments suggest the promise of the proposed method.

• The Journal of the Acoustical Society of Korea
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• v.23 no.2
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• pp.173-179
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• 2004

Due to the insufficiency of training data in large vocabulary continuous speech recognition, similar context dependent phones can be clustered by decision trees to share the data. When the decision trees are built and used to predict unseen triphones, a phonetic question set is required. The phonetic question set, which contains categories of the phones with similar co-articulation effects, is usually generated by phonetic or linguistic experts. This knowledge-based approach for generating phonetic question set, however, may reduce the homogeneity of the clusters. Moreover, the experts must adjust the question sets whenever the language or the PLU (phone-like unit) of a recognition system is changed. Therefore, we propose a data-driven method to automatically generate phonetic question set. Since the proposed method generates the phone categories using speech data distribution, it is not dependent on the language or the PLU, and may enhance the homogeneity of the clusters. In large vocabulary speech recognition experiments, the proposed algorithm has been found to reduce the error rate by 14.3%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1396-1412
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• 2023

Conversation modeling is an important and challenging task in the field of natural language processing because it is a key component promoting the development of automated humanmachine conversation. Most recent research concerning conversation modeling focuses only on the current utterance (considered as the current question) to generate a response, and thus fails to capture the conversation's logic from its beginning. Some studies concatenate the current question with previous conversation sentences and use it as input for response generation. Another approach is to use an encoder to store all previous utterances. Each time a new question is encountered, the encoder is updated and used to generate the response. Our approach in this paper differs from previous studies in that we explicitly separate the encoding of the question from the encoding of its context. This results in different encoding models for the question and the context, capturing the specificity of each. In this way, we have access to the entire context when generating the response. To this end, we propose a deep neural network-based model, called the Context Model, to encode previous utterances' information and combine it with the current question. This approach satisfies the need for context information while keeping the different roles of the current question and its context separate while generating a response. We investigate two approaches for representing the context: Long short-term memory and Convolutional neural network. Experiments show that our Context Model outperforms a baseline model on both ConvAI2 Dataset and a collected dataset of conversational English.

• Journal of Korean Medical classics
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• v.22 no.4
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• pp.189-198
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• 2009

Considering the opinions of annotators, the remedy about excess of east and deficiency of west from "the seventy-fifth question" can be arranged as follows. "The seventy-fifth question", with "the sixty-ninth question", explains excess and deficiency of mother and son. Abatement of fire and invigoration of water[瀉火補水] in the excess of wood and deficiency of metal[木實金虛] presents a remedy, which has been applied in herbs and medicine application henceforth. "The seventy-fifth question" is a unique theory from " Nan-gyeong(難經)", and does not continue the theory of "Hwangjenaegyeong(黃帝內經)". "The seventy-fifth question" mentions the relationship of excess and deficiency of the five elements and five viscera, but does not mention excess and deficiency of invigoration and abatement of the meridian. Remedy from abatement of fire and invigoration of water[瀉火補水] in the excess of wood and deficiency of metal[木實金虛] is an abnormal, temporary and extraordinary method. This remedy is applied in Saam acupuncture[舍巖鍼] as A-variation form. The process where Son allows excess of mother[子能令母實] and mother allows deficiency of son[母能令子虛] in the abatement of fire and invigoration of water[瀉火補水] is a mechanism, not a remedy. Generation after generation, medical practitioners can be classified into those that claimed abatement of fire and invigoration of water[瀉火補水] because of the relation with excess of liver and deficiency of lung[肝實肺虛], abatement of heart(瀉心) due to the excess of liver(肝實), or invigoration of Eum and abatement of Yang[補陰瀉陽].

• Journal of the Korean Society of Earth Science Education
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• v.9 no.3
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• pp.255-268
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• 2016

The education should adapt learners well to any changes and have them create something for such a era. Form the point of this view. question sheet was developed for middle school students to improve their scientific creativity. For this study, 146 item questions, which was from chapter 7 about solar system movement in the 3rd grade textbook for middle school students, was developed. For 5 weeks, 142 third graders in middle school were chosen and observed. They were divided into an experimental group and a control group. The teaching model using question sheet was applied to the experimental group, while the traditional teaching model, to the control group. This study compared two groups based on scientific creativity and academic achievement. In both scientific creativity and academic achievement, the group using question sheet showed meaningful differences. This result of the analysis indicated that teaching model using question sheet stimulated student's creative thinking and helped them to achieve a goal of lesson. The teaching model using question sheet can be used as an effective way to increase students' creativity.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.423-431
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• 2011

For procuring more sensitive information and estimating stratum target population proportion as well as an overall one form a sensitive population composed of several strata we suggest a two-stage stratified unrelated question model that uses stratified random sampling instead of simple random sampling in the two-stage unrelated question model by Kim et al. (1992) and extend it to the three-stage stratified unrelated question model. We also deal with the proportional and optimal allocation problems in each suggested model, compare the relative efficiency of the suggested two models, and show that the three-stage stratified unrelated question model is more efficient than the two-stage one in view of the variance.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.209-222
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• 2013

We apply stratified cluster sampling to a replicated systematic unrelated question model for a large scale survey in which the population is comprised of several strata developed by several clusters and with sensitive parameters. We first present a replicated systematic unrelated question model using an unrelated question model to procure sensitive information from the population of clusters and then develop a suggested model to an unrelated question by a stratified cluster replicated systematic sampling that can be used in large population of strata. We cover the proportional and optimum allocation for the suggested model. Finally, we compare and analyze the efficiency of the suggested model with the replicated systematic unrelated question model.

• The Journal of the Korea Contents Association
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• v.7 no.7
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• pp.50-59
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• 2007

This paper describes a question analysis and expansion based on semantics for on efficient information retrieval. Results of all information retrieval systems include many non-relevant documents because the index cannot naturally reflect the contents of documents and because queries used in information retrieval systems cannot represent enough information in user's question. To solve this problem, we analyze user's question semantically, determine the answer type, and extract semantic features. And then we expand user's question using them and syntactic structures which are used to represent the answer. Our similarity is to rank documents which include expanded queries in high position. Especially, we found that an efficient document retrieval is possible by a question analysis and expansion based on semantics on natural language questions which are comparatively short but fully expressing the information demand of users.