• Title/Summary/Keyword: question/answer

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Regional Culture Contents Service Modeling Based On Localized Advertising of Question And Answer Format (위치문답형 지역광고 기반의 문화정보 서비스 모델링)

  • Shin, Hwan-Seob;Lee, Jae-Won
    • The Journal of the Korea Contents Association
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    • v.19 no.8
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    • pp.465-472
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    • 2019
  • Although there are various cultural events and cultural contents produced in the region, there is a lack of distribution and spread of regional information to expand related economic consumption. This study combined local advertising by local advertisers with the knowledge search method in question and answer format from a location-based service perspective for the purpose of spreading and using local cultural information. The approach looked at domestic and international cases of knowledge search based on region and location-based advertising research, presented community model of location inquiry based information service and revenue model of local advertisement. Through this, this study designed a question and answer based community and operational structure model of local advertising, and developed an information service system in the form of prototyping. By extending the distribution of question and answer data among users to location information, it is meaningful that a business service model was presented that combines local cultural content information and the demand for user access with the revenue model of local advertising.

A Extraction of Definitional Answer Sentence for a Definitional Question-Answering System (정의형 질의응답시스템을 위한 정의형 정답 문장 추출)

  • Ko, Byeong Il;Kang, Yu Hwan;Shin, Seung Eun;S, Young Hoon
    • Proceedings of the Korea Contents Association Conference
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    • 2004.11a
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    • pp.470-475
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    • 2004
  • In this paper, we propose a method to extract a definitional answer sentence for a Definitional Question-Answering System. definitional answer sentence patterns are manually constructed with restriction rules to patterns, and a ranking information of the pattern using its frequency from the corpus. answer sentence pattern consists of the syntactic structure of a definitional answer sentence, and clue words. this system show 83% accuracy for untrained corpus.

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Classroom Discourse Analysis between Teacher and Students in Science Classroom (과학 수업 시간에 발생하는 교사-학생 간 교실 담화 분석)

  • Han, Shin;Jung, Jinwoo
    • Journal of Science Education
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    • v.35 no.2
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    • pp.159-172
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    • 2011
  • The purpose of this study is to specify the quality of I-R-E pattern and question-answer affiliation between students and teacher, depending on teacher's career. This study analyzes 6 classroom discourse texts of 6th grade science class. The results of this study are as follows. First, in the case of a newly appointed teacher, I-R-E pattern is appeared repeatedly. Second, in the case of experienced teachers, expended I-R-E pattern is appeared compare with a newly appointed teacher. Third, in the case of a newly appointed teacher, independence relational structure is appeared more repeatedly than other structures. But, in the case of experienced teachers, all kinds of question-answer structures - independence, parallel, insertion, and reorganization relational structure - are appeared more evenly.

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A Framework for Q&A Community based Vertical Search (Q&A 커뮤니티 기반 전문영역 검색을 위한 프레임워크)

  • Jeong, Ok-Ran;Oh, Je-Hwan;Lee, Eun-Seok
    • The Journal of Society for e-Business Studies
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    • v.16 no.2
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    • pp.143-158
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    • 2011
  • This study suggests a framework which extracts features of collective intelligence from social Q&A community sites and takes advantage of those features upon vertical search for domain specific knowledge or information retrieval. One source of collective intelligence on the internet is the question and answer(Q&A) data available from many Q&A sites. Vertical search is focused on searching special areas or specific domains. This paper proposes a framework for extending the relevant terms by using Q&A information connected with query that the user wants to retrieve, and then applies them to specific domain field that requires professional and detailed knowledge.

Question-Answering System using the Superlative Words (최상급 단서 어휘를 이용한 질의-응답시스템)

  • Park, Hee-Geun;Oh, Su-Hyun;Ahn, Young-Min;Seo, Young-Hoon
    • Proceedings of the Korea Contents Association Conference
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    • 2006.05a
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    • pp.140-143
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    • 2006
  • In this paper, we describe a question-answering system which extracts answers for the superlative questions which include the superlative words such as "the most", "the best", "the first", "the largest", "the least", and so on. The superlative questions are composed of four main components and others. Four main components are the superlative word, answer type, regional information, and a verb modified by the superlative word. We classify the superlative words into two types as to whether the verb has to be needed to be a question or not. The superlative word, answer type and regional information are essential elements to extract answer for all superlative questions. But the verb may be an essential element by the type of superlative word. Our system analyzes input question, and finds four main components of the superlative question. Also, our system searches relative documents and candidate sentences using them, and extracts answers from candidate sentences. Empirical result shows that our system has high precision and high recall for the superlative questions.

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Topic modeling for automatic classification of learner question and answer in teaching-learning support system (교수-학습지원시스템에서 학습자 질의응답 자동분류를 위한 토픽 모델링)

  • Kim, Kyungrog;Song, Hye jin;Moon, Nammee
    • Journal of Digital Contents Society
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    • v.18 no.2
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    • pp.339-346
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    • 2017
  • There is increasing interest in text analysis based on unstructured data such as articles and comments, questions and answers. This is because they can be used to identify, evaluate, predict, and recommend features from unstructured text data, which is the opinion of people. The same holds true for TEL, where the MOOC service has evolved to automate debating, questioning and answering services based on the teaching-learning support system in order to generate question topics and to automatically classify the topics relevant to new questions based on question and answer data accumulated in the system. Therefore, in this study, we propose topic modeling using LDA to automatically classify new query topics. The proposed method enables the generation of a dictionary of question topics and the automatic classification of topics relevant to new questions. Experimentation showed high automatic classification of over 0.7 in some queries. The more new queries were included in the various topics, the better the automatic classification results.