• English Language & Literature Teaching
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• v.8 no.2
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• pp.105-115
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• 2003

The four skills of a language are basically required for a communication. They are very important for a learner to develop the balanced language acquisition. Today both listening and speaking skills are emphasized in the global era rather than reading and writing proficiencies. The reason is really why the learners' communicative competence is more needed than the accurate knowledge of a structure in the language. For this reason, the listening comprehension should be taught effectively using the following strategies. First, the sound difference of a language must be taught. Language is a complicated process to convey the comprehensive meaning combined with the internal and external factors of a language. In other words, the meaning for the sound of language should be transmitted by the unit of vocabulary and syntax. Second, a good listening comprehension requires the familiarity and much experience with a lot of English words to understand English sentences unconsciously. Third, as understanding the structure of language is effective for the listening comprehension, the better listening comprehension can be possible through the meaningful exercise. Fourth, the compound process of listening comprehension requires the comprehensive understanding of language, but not the separate understanding of language. Fifth, the appropriate application of the multimedia courseware helps improve the listening comprehension better than that of the existing audio, video, tape recorder and so on. Using multimedia courseware is useful as follows: A learner is able to take as much lesson as he/she wants. It does take little time to repeat about what he/she takes a lesson. It gives the lively picture with the native speakers' voices. It gives him/her(a learner) a feedback effect continuously through the interaction of computer. It controls his/her lesson in accordance with the level of a learner.

• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.630-638
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• 2015

This study examines university students' reading strategies based on English proficiency and reading anxiety. A total of 86 university students participated in the study and completed two questionnaires related to reading strategies[9] and reading anxiety[10]. The collected data was quantitatively analyzed using SPSS 21. The results indicated that advanced and intermediate students preferred to use Problem Solving Strategies(PROB), whereas beginning level readers tended to use and Global Reading strategies(GLOB). There was no significant relationship between reading anxiety and reading strategies. However, there were some differences between two groups. High anxiety students preferred to use reading materials and textual clues and try to focus on their reading when they have a difficulty. Low anxiety students tended to adjust reading pace and try to focus on comprehension and reread when they lost their focus. Based on the results of the study, several implications and limitations were discussed.

• English Language & Literature Teaching
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• v.13 no.3
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• pp.157-176
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• 2007

Based on the consensus that vocabulary plays an essential role in improving reading ability, the present study investigates the effects of two different methods of English vocabulary instruction - the traditional wordlist-based vocabulary instruction (G1) and the collocation-based vocabulary instruction (G2) - on improving English reading ability for high school learners. First, the results of the collocation test revealed that G1 and G2 groups attained similar mean scores on the pretests; however, G2 significantly outperformed G1 on the post-test administered at the end of the learning activity. The results proved that students who learned vocabulary in a collocation-oriented setting had better retention through the lexical-chunking achieved by the collection method. Second, in terms of reading comprehension achieved on nationwide academic trial tests, the result was, after considering more tests, that G2 gained a significantly higher mean score on the third and fourth than G1 did, although the two groups yielded a similar mean score on the first and second test. The implication of these results is that collocation-based vocabulary activities had a more significant influence on reading skills than wordlist-oriented ones.

• Journal of Korean Library and Information Science Society
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• v.42 no.4
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• pp.111-136
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• 2011

In the 90s, due the Japanese students' degradation in reading comprehension from literacy escape, it was serious social phenomenon that brought reading crisis in Japan. However, school libraries and related institutions regarded this as a turning point and started to reset revise their legislations and focused on environmental improvement of children's reading activities from various aspects by conduction reading related activities. This study is to help understanding macro-environment of Japanese school libraries by introducing and summarizing all the efforts from each fields for restructuring reading environment mainly around the school libraries from 90's in Japan.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.35-40
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• 2017

기계 독해(Machine reading comprehension)는 주어진 문맥을 이해하고, 질문에 적합한 답을 문맥 내에서 찾는 문제이다. Simple Recurrent Unit (SRU)은 Gated Recurrent Unit (GRU)등과 같이 neural gate를 이용하여 Recurrent Neural Network (RNN)에서 발생하는 vanishing gradient problem을 해결하고, gate 입력에서 이전 hidden state를 제거하여 GRU보다 속도를 향상시킨 모델이며, Self-matching Network는 R-Net 모델에서 사용된 것으로, 자기 자신의 RNN sequence에 대하여 어텐션 가중치 (attention weight)를 계산하여 비슷한 의미 문맥 정보를 볼 수 있기 때문에 상호참조해결과 유사한 효과를 볼 수 있다. 본 논문에서는 한국어 기계 독해 데이터 셋을 구축하고, 여러 층의 SRU를 이용한 Encoder에 Self-matching layer를 추가한 $S^2$-Net 모델을 제안한다. 실험 결과, 본 논문에서 제안한 $S^2$-Net 모델이 한국어 기계 독해 데이터 셋에서 EM 65.84%, F1 78.98%의 성능을 보였다.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.35-40
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• 2017

기계 독해(Machine reading comprehension)는 주어진 문맥을 이해하고, 질문에 적합한 답을 문맥 내에서 찾는 문제이다. Simple Recurrent Unit (SRU)은 Gated Recurrent Unit (GRU)등과 같이 neural gate를 이용하여 Recurrent Neural Network (RNN)에서 발생하는 vanishing gradient problem을 해결하고, gate 입력에서 이전 hidden state를 제거하여 GRU보다 속도를 향상시킨 모델이며, Self-matching Network는 R-Net 모델에서 사용된 것으로, 자기 자신의 RNN sequence에 대하여 어텐션 가중치 (attention weight)를 계산하여 비슷한 의미 문맥 정보를 볼 수 있기 때문에 상호참조해결과 유사한 효과를 볼 수 있다. 본 논문에서는 한국어 기계 독해 데이터 셋을 구축하고, 여러 층의 SRU를 이용한 Encoder에 Self-matching layer를 추가한 $S^2$-Net 모델을 제안한다. 실험 결과, 본 논문에서 제안한 $S^2$-Net 모델이 한국어 기계 독해 데이터 셋에서 EM 65.84%, F1 78.98%의 성능을 보였다.

• Industrial Engineering and Management Systems
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• v.13 no.2
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• pp.185-192
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• 2014

This study stems from the work of Lehman et al. (Ergonomics, 2001) which concluded that standing yields better work performance, and from the growing health trend of recommending the reduction of the amount of time spent in sitting (Owen et al., Exercise and Sport Science Reviews, 2010). Lajoie et al. (Experimental Brain Research, 1993) provided an initial significant contribution to a theory that standing requires a person to control balance, equating to demand higher productive output from the cognitive system than when a person is sitting. An assumption was formulated that standing position during class is feasible and can be adopted on the belief that it might contribute positive results to students' performance. The purpose of this study is to identify whether a body position during exams tested along with exposure durations has a significant effect on college students' performance. Mathematical analysis and reading comprehension exam was used to measure the cognitive performance of the students. Two factors, position and duration, were tested for significance with two levels each subjected to six replicates. Twenty-four students from the College of Engineering and Agro-Industrial Technology, University of the Philippines Los Ba$\tilde{n}$os were randomly selected. The experiment showed that the body position during exams is a significant factor for the Math exam, but insignificant for the Reading Comprehension exam.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.139-143
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• 2021

최근 대용량 말뭉치를 기반으로 한 언어 모델이 개발됨에 따라 다양한 자연어처리 분야에서 사람보다 높은 성능을 보이는 시스템이 제안되었다. 이에 따라, 더 어렵고 복잡한 문제를 해결하기 위한 데이터셋들이 공개되었으며 대표적으로 기계독해 작업에서는 시스템이 질문에 대해 답변할 수 없다고 판단할 수 있는지 평가하기 위한 데이터셋이 공개되었다. 입력 받은 데이터에 대해 답변할 수 없다고 판단하는 것은 실제 애플리케이션에서 중요한 문제이기 때문에, 이를 해결하기 위한 연구도 다양하게 진행되었다. 본 논문에서는 문서를 이해하여 답변할 수 없는 데이터에 대해 효과적으로 판단할 수 있는 기계독해 시스템을 제안한다. 제안 모델은 문서의 내용과 질문에 대한 이해도가 낮을 경우 정확한 정답을 맞히지 못하는 사람의 독해 패턴에서 착안하여 기계독해 시스템의 문서 이해도를 높이고자 한다. KLUE-MRC 개발 데이터를 통한 실험에서 EM, Rouge-w 기준으로 각각 71.73%, 76.80%을 보였다.

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.73-79
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• 2024

The smart tourism chatbot service provide smart tourism services to users easily and conveniently along with the smart tourism app. In this paper, the tourism information QA (Question Answering) service is proposed based on the task-oriented smart tourism chatbot system [13]. The tourism information QA service is an MRC (Machine reading comprehension)-based QA system that finds answers in context and provides them to users. The tourism information QA system consists of NER (Named Entity Recognition), DST (Dialogue State Tracking), Neo4J graph DB, and QA servers. We propose tourism information QA service uses the tourism information NER model and DST model to identify the intent of the user's question and retrieves appropriate context for the answer from the Neo4J tourism knowledgebase. The QA model finds answers from the context and provides them to users through the smart tourism app. We develop the tourism information QA model by transfer learning the bigBird model, which can process the context of 4,096 tokens, using the tourism information QA dataset.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.153-161
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• 2013

Recently, there has been a tremendous increase in the availability of educational materials for foreign language learning. As part of this trend, there has been an increase in the amount of electronically mediated materials available. However, conventional educational contents developed using computer technology has provided typically one-way information, which is not the most helpful thing for users. Providing the user's convenience requires additional off-line analysis for diagnosing an individual user's learning. To improve the user's comprehension of texts written in a foreign language, we propose an intelligent learning tool based on the analysis of the user's eyeball movements, which is able to diagnose and improve foreign language reading ability by providing necessary supplementary aid just when it is needed. To determine the user's learning state, we correlate their eye movements with findings from research in cognitive psychology and neurophysiology. Based on this, the learning tool can distinguish whether users know or do not know words when they are reading foreign language sentences. If the learning tool judges a word to be unknown, it immediately provides the student with the meaning of the word by extracting it from an on-line dictionary. The proposed model provides a tool which empowers independent learning and makes access to the meanings of unknown words automatic. In this way, it can enhance a user's reading achievement as well as satisfaction with text comprehension in a foreign language.