Introducing LUIMA: an experiment in legal conceptual retrieval of vaccine injury decisions using a UIMA type system and tools

Matthias Grabmair, Kevin D. Ashley, Ran Chen, Preethi Sureshkumar, Chen Wang, Eric Nyberg, Vern R. Walker
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引用次数: 54

Abstract

This paper presents first results from a proof of feasibility experiment in conceptual legal document retrieval in a particular domain (involving vaccine injury compensation). The conceptual markup of documents is done automatically using LUIMA, a law-specific semantic extraction toolbox based on the UIMA framework. The system consists of modules for automatic sub-sentence level annotation, machine learning based sentence annotation, basic retrieval using Apache Lucene and a machine learning based reranking of retrieved documents. In a leave-one-out experiment on a limited corpus, the resulting rankings scored higher for most tested queries than baseline rankings created using a commercial full-text legal information system.
介绍LUIMA:使用UIMA类型的系统和工具进行疫苗伤害判决法律概念检索的实验
本文介绍了在特定领域(涉及疫苗伤害赔偿)概念法律文件检索的可行性证明实验的初步结果。文档的概念标记是使用LUIMA自动完成的,LUIMA是一个基于UIMA框架的特定于法律的语义提取工具箱。该系统由自动子句级标注、基于机器学习的句子标注、使用Apache Lucene的基本检索和基于机器学习的检索文档重新排序模块组成。在有限语料库上的“留一”实验中,大多数测试查询的结果排名得分高于使用商业全文法律信息系统创建的基线排名。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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