Performance Comparison Analysis on Named Entity Recognition system with Bi-LSTM based Multi-task Learning

다중작업학습 기법을 적용한 Bi-LSTM 개체명 인식 시스템 성능 비교 분석

  • Kim, GyeongMin (Department of Computer Science and Engineering, Korea University) ;
  • Han, Seunggnyu (Department of Computer Science and Engineering, Korea University) ;
  • Oh, Dongsuk (Department of Computer Science and Engineering, Korea University) ;
  • Lim, HeuiSeok (Department of Computer Science and Engineering, Korea University)
  • 김경민 (고려대학교 컴퓨터학과) ;
  • 한승규 (고려대학교 컴퓨터학과) ;
  • 오동석 (고려대학교 컴퓨터학과) ;
  • 임희석 (고려대학교 컴퓨터학과)
  • Received : 2019.10.28
  • Accepted : 2019.12.20
  • Published : 2019.12.28


Multi-Task Learning(MTL) is a training method that trains a single neural network with multiple tasks influences each other. In this paper, we compare performance of MTL Named entity recognition(NER) model trained with Korean traditional culture corpus and other NER model. In training process, each Bi-LSTM layer of Part of speech tagging(POS-tagging) and NER are propagated from a Bi-LSTM layer to obtain the joint loss. As a result, the MTL based Bi-LSTM model shows 1.1%~4.6% performance improvement compared to single Bi-LSTM models.


Supported by : Korea Creative Content Agency(KOCCA)


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