• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.568-570
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• 2021

기계독해 시스템은 주어진 질문에 대한 답변을 문서에서 찾아 사용자에게 제공해주는 질의응답 작업 중 하나이다. 하지만 대부분의 기계독해 데이터는 간결한 답변 추출을 다루며, 이는 실제 애플리케이션에서 유용하지 않을 수 있다. 실제 적용 단계에서는 짧고 간결한 답변 뿐 아니라 사용자에게 자세한 정보를 제공해줄 수 있는 긴 길이의 답변 제공도 필요하다. 따라서 본 논문에서는 짧은 답변과 긴 답변 모두 추출할 수 있는 모델을 제안한다. 실험을 통해 Baseline과 비교하여 짧은 답변 추출에서는 F1 score 기준 0.7%, 긴 답변 추출에는 1.4%p의 성능 향상을 보이는 결과를 얻었다.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.249-255
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• 2014

Recently, creation and sharing of information make progress actively through the SNS(Social Network Service) such as twitter, facebook and so on. It is necessary to extract the knowledge from aggregated information and data mining is one of the knowledge based approach. Especially, emotion analysis is a recent subdiscipline of text classification, which is concerned with massive collective intelligence from an opinion, policy, propensity and sentiment. In this paper, We propose the emotion prediction method, which extracts the significant key words and related key words from SNS paragraph, then predicts the emotion using these extracted emotion features.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.267-273
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• 2016

Creation and Sharing of information which is structured data as well as various unstructured data. makes progress actively through the spread of mobile. Recently, Big Data extracts the semantic information from SNS and data mining is one of the big data technique. Especially, the general emotion analysis that expresses the collective intelligence of the masses is utilized using large and a variety of materials. In this paper, we propose the emotion prediction system architecture which extracts the significant keywords from social network paragraphs using n-gram and Korean morphological analyzer, and predicts the emotion using SVM and these extracted emotion features. The proposed system showed 82.25% more improved recall rate in average than previous systems and it will help extract the semantic keyword using morphological analysis.