01480nam a2200337 c 4500001001300000005001500013007000300028008004100031020002500072049002800097052001800125056001400143074002500157082001500182245015500197260006400352300002300416490003700439500009200476500009300568650004700661650004200708650004500750650004700795700002700842830007300869880008500942880007401027880002701101950001401128KMO20224959020221111100523ta220719s2022    hck        HB 000   kor    a9791162981221g933500 lEM8309321lEM8309322c201a367.43b22-14  a367.4326  k11-1332522-000116-0101a363.25223006880-01a딥러닝을 활용한 진술조서 수사정보 추출 연구 :bKOBERT 개체명 인식 모델을 중심으로 /d연구책임자: 김혜진  6880-02a아산 :b경찰대학 치안정책연구소,c2022  aii, 33 p. ;c26 cm10a책임연구보고서 ;v2021-08  aKOBERT는 "Korean Bidirectional Encoder Representations from Transformers'의 약어임  a권말부록: 1. Victim info parsing ; 2. Crime report parsing ; 3. Offense info parsing 8a수사 방법[搜査方法]0KSH1999010788 8a수사(범죄)[搜査]0KSH1998005192 8a딥 러닝[deep learning]0KSH2016000040 8a진술 조서[陳述調書]0KSH19990037241 6880-03a김혜진4aut 0a책임연구보고서(경찰대학 치안정책연구소) ;v2021-08006245-01/(BaDip reoning eul hwaryong han jinsul joseo susa jeongbo chuchul yeongu  6260-02/(BaAsan :bGyeongchal Daehak Chian Jeongchaek Yeonguso,c20221 6700-03/(BaGim, Hyejin1 a비매품