• Title/Summary/Keyword: ETRI Exobrain

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A Study on Automatic Classification of Record Text Using Machine Learning (기계학습을 이용한 기록 텍스트 자동분류 사례 연구)

  • Kim, Hae Chan Sol;An, Dae Jin;Yim, Jin Hee;Rieh, Hae-Young
    • Journal of the Korean Society for information Management
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    • v.34 no.4
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    • pp.321-344
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    • 2017
  • Research on automatic classification of records and documents has been conducted for a long time. Recently, artificial intelligence technology has been developed to combine machine learning and deep learning. In this study, we first looked at the process of automatic classification of documents and learning method of artificial intelligence. We also discussed the necessity of applying artificial intelligence technology to records management using various cases of machine learning, especially supervised methods. And we conducted a test to automatically classify the public records of the Seoul metropolitan government into BRM using ETRI's Exobrain, based on supervised machine learning method. Through this, we have drawn up issues to be considered in each step in records management agencies to automatically classify the records into various classification schemes.

Korean Semantic Role Labeling Using Semantic Frames and Synonym Clusters (의미 프레임과 유의어 클러스터를 이용한 한국어 의미역 인식)

  • Lim, Soojong;Lim, Joon-Ho;Lee, Chung-Hee;Kim, Hyun-Ki
    • Journal of KIISE
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    • v.43 no.7
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    • pp.773-780
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    • 2016
  • Semantic information and features are very important for Semantic Role Labeling(SRL) though many SRL systems based on machine learning mainly adopt lexical and syntactic features. Previous SRL research based on semantic information is very few because using semantic information is very restricted. We proposed the SRL system which adopts semantic information, such as named entity, word sense disambiguation, filtering adjunct role based on sense, synonym cluster, frame extension based on synonym dictionary and joint rule of syntactic-semantic information, and modified verb-specific numbered roles, etc. According to our experimentations, the proposed present method outperforms those of lexical-syntactic based research works by about 3.77 (Korean Propbank) to 8.05 (Exobrain Corpus) F1-scores.