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NLP-based Metadata Extraction for Legal Text Consolidation

机译:基于NLP的元数据提取,用于法律文本合并

摘要

The paper describes a system for the automatic consolidation of Italian legislative texts to be used as a support of an editorial consolidating activity and dealing with the following typology of textual amendments: repeal, substitution and integration. The focus of the paper is on the semantic analysis of the textual amendment provisions and the formalized representation of the amendments in terms of metadata. The proposed approach to consolidation is metadata- oriented and based on Natural Language Processing (NLP) techniques: we use XML-based standards for metadata annotation of legislative acts and a flexible NLP architecture for extracting metadata from parsed texts. An evaluation of achieved results is also provided.
机译:本文介绍了一种自动合并意大利立法文本的系统,该系统将用于支持编辑合并活动并处理以下文本修正类型:废除,替代和合并。本文的重点是对文本修订条款的语义分析以及以元数据的形式对修订进行形式化表示。提议的整合方法是面向元数据的,并且基于自然语言处理(NLP)技术:我们使用基于XML的标准来立法行为的元数据注释,并使用灵活的NLP体系结构来从解析的文本中提取元数据。还提供了对已实现结果的评估。

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