112 긴급신고 시스템은 국민 안전을 위한 경찰의 최전선으로, 신속한 출동 및 사건처리가 무엇보다도 중요하다. 허위오인신고는 경찰력이 낭비될뿐 아니라 진정으로 도움을 필요로 하는 상황에 대응하기가 어려워진다는 점에서 그 문제가 크다. 최근들어 증가하고 있는 허위오인신고에 대응하기 위하여 본 연구자는 딥러닝을 기반으로한 112 허위오인신고 분류·예측모델을 제안한다. 본 모델은 112상황실 접수요원이 요약한 신고내용 텍스트를 입력받아 해당 신고의 허위오인여부를 결정하게 된다. 악의적 허위신고와 오인신고중 후자는 신고접수자 입장에서 표면적으로 알아채기가 불가능에 가깝다. 이로 인해 연구자는 허위오인신고 전체 데이터와 악의적인 허위신고만을 담은 데이터를 나누어 실험하였다. 트랜스포머 구조를 가진 BERT, KoBERT,ELECTRA, KoELECTRA, RoBERTa 5가지 모델에 대하여 동일한 하이퍼파라미터로 모델 훈련을 진행했다. 본 연구는 자연어처리와 LLM을 이용하여 경찰의 실제 치안업무에 대해 문제해결적 접근을 수행했다는 점에 의의가 있다. 본 연구의 결과가 악의적인 허위신고를 빠르게 식별하고 경찰이 의사결정을 지원하는데에 도움이 될 것으로 기대한다.
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