Automated Attribute Extraction from Legal Proceedings
원문 초록
The escalating number of pending cases is a growing concern world-wide. Recent advancements in digitization have opened up possibilities for leveraging artificial intelligence (AI) tools in the processing of legal documents. Adopting a structured representation for legal documents, as opposed to a mere bag-of-words flat text representation, can significantly enhance processing capabilities. With the aim of achieving this objective, we put forward a set of diverse attributes for criminal case proceedings. We use a state-of-the-art sequence labeling framework to automatically extract attributes from the legal documents. Moreover, we demonstrate the efficacy of the extracted attributes in a downstream task, namely legal judgment prediction.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Attributes Shape the Embedding Space of Face Recognition Models
Ref-Long: Benchmarking the Long-context Referencing Capability of Long-context Language Models
Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations
MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection
The Trilemma of Truth in Large Language Models
Style-Aligned Image Composition for Robust Detection of Abnormal Cells in Cytopathology
토론
이 논문과 연결된 포럼 글이 아직 없습니다.