Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Tax Judgment Analysis and Prediction using NLP and BiLSTM

NLP와 BiLSTM을 적용한 조세 결정문의 분석과 예측

  • Lee, Yeong-Keun (Dept. of Computer Engineering, Kongju National University) ;
  • Park, Koo-Rack (Dept. of Computer Science & Engineering, Kongju National University) ;
  • Lee, Hoo-Young (Dept. of Computer Engineering, Kongju National University)
  • 이영근 (공주대학교 컴퓨터공학과) ;
  • 박구락 (공주대학교 컴퓨터공학부) ;
  • 이후영 (공주대학교 컴퓨터 공학과)
  • Received : 2021.08.31
  • Accepted : 2021.09.20
  • Published : 2021.09.28
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Abstract

Research and importance of legal services applied with AI so that it can be easily understood and predictable in difficult legal fields is increasing. In this study, based on the decision of the Tax Tribunal in the field of tax law, a model was built through self-learning through information collection and data processing, and the prediction results were answered to the user's query and the accuracy was verified. The proposed model collects information on tax decisions and extracts useful data through web crawling, and generates word vectors by applying Word2Vec's Fast Text algorithm to the optimized output through NLP. 11,103 cases of information were collected and classified from 2017 to 2019, and verified with 70% accuracy. It can be useful in various legal systems and prior research to be more efficient application.

일반인에게 난해한 법률분야를 이해하기 쉽고 예측 가능 할 수 있도록 인공지능을 적용한 법률 서비스에 대한 연구의 중요성이 대두되고 있다. 본 연구에서는 조세심판원의 결정정보를 수집하고 데이터 처리와 자체 학습을 통한 모델을 구축하여 사용자의 질의에 맞는 답변을 예측하기 위한 시스템을 제안한다. 제안 모델은 웹크롤링을 통해서 조세 결정문의 정보 수집 및 자연어 처리과정을 통하여 유용한 데이터를 추출하고, 최적화된 산출물을 Word2Vec의 Fast Text 알고리즘을 적용하여 단어의 벡터를 생성하였다. 2017년부터 2019년까지 총 11,103건의 정보를 수집하고 분류하였으며 RNN 기술의 BiLSTM을 적용하여 자체학습을 통한 결과 예측 프로그램을 구축하여 70%정확도로 실증하였다. 향후 다양한 법률시스템으로 활용성을 기대할 수 있으며 보다 효율적인 적용을 위한 연구와 정확도 향상을 위한 연구가 계속되어야 한다.

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