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- W4322773090 abstract "자연어 처리 기술을 이용해 진술의 신빙성을 판별하는 소프트웨어에 대한 연구가 대두되고 있다. 이때 피해자의 진술에서 빈번히 생략되는 주어의 정확한 복원이 이루어진다면 컴퓨터의 정확한 문장 이해를 통해 진술신빙성 판단 성능을 높일 수 있다. 기존 한국어 무형대용어 해결 연구에서 주로 사용되었던 머신러닝 및 딥러닝 모델은 명사구에 초점을 맞춘 것으로, 일정 범위 내에 선행어가 존재하여야 생략어 복원이 가능하다는 등의 한계점이 있다. 성폭력 피해자 면담 조사 진술서의 특성상 서술어에 초점을 맞춘다면 효율적인 복원이 가능하다. 이에 기반해 본 연구에서는 구문 분석기와 규칙기반 모델을 이용해 생략된 가해자 또는 피해자 주어를 문장 단위로 복원하는 방법을 채택했다. 구축한 모델로 실제 성폭력 피해자 조사 면담 기록물에서 피해자 또는 가해자의 주어가 생략된 2,396문장에 대해 주어 복원 실험을 진행하였다. 본 모델의 복원 성능은 F1 스코어는 90.79%이며 낮지 않은 수준으로 판단한다. Research on software that determines the credibility of statements using natural language processing technology is emerging. If accurate restoration of subjects that are frequently omitted from victims statements is performed, the performance of judging the credibility of statements can be improved through accurate sentence understanding of the computer. Machine learning and deep learning models, which were mainly used in existing research on Korean Zero Anaphora Resolution (ZAR), have limitations such that anaphora can be restored only when preceding words exist. Due to the nature of the investigative interview, efficient sentence-wise restoration is possible based on the predicate. Therefore, this study adopts a method of restoring omitted perpetrators or victims as the subject on a sentence-by-sentence basis using parsers and a rule-based model. We conducted an experiment using 2,396 sentences. Our model outperforms and its F1 score is 90.79%, which is considered decent for the credibility check system." @default.
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- W4322773090 date "2023-02-28" @default.
- W4322773090 modified "2023-10-16" @default.
- W4322773090 title "Restoring the Subjects in Sexual Violence Victim’s Statements by Sentence-wise Rule-based Zero Anaphora Resolution" @default.
- W4322773090 doi "https://doi.org/10.14801/jkiit.2023.21.2.43" @default.
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