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首页> 外文期刊>Artificial Intelligence and Law >Unsupervised and supervised text similarity systems for automated identification of national implementing measures of European directives
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Unsupervised and supervised text similarity systems for automated identification of national implementing measures of European directives

机译：欧洲指令全国实施措施的自动识别无监督和监督文本相似性系统

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摘要

The automated identification of national implementations (NIMs) of European directives by text similarity techniques has shown promising preliminary results. Previous works have proposed and utilized unsupervised lexical and semantic similarity techniques based on vector space models, latent semantic analysis and topic models. However, these techniques were evaluated on a small multilingual corpus of directives and NIMs. In this paper, we utilize word and paragraph embedding models learned by shallow neural networks from a multilingual legal corpus of European directives and national legislation (from Ireland, Luxembourg and Italy) to develop unsupervised semantic similarity systems to identify transpositions. We evaluate these models and compare their results with the previous unsupervised methods on a multilingual test corpus of 43 Directives and their corresponding NIMs. We also develop supervised machine learning models to identify transpositions and compare their performance with different feature sets.
机译：通过文本相似性技术自动识别欧洲指令的国家实施（NIM）已经显示出有前途的初步结果。以前的作品提出并利用了基于矢量空间模型，潜在语义分析和主题模型的无监督的词汇和语义相似性技术。然而，这些技术是对小型指令和尼斯的小型多语种语料库进行评估。在本文中，我们利用了浅层神经网络从欧洲指令和国家立法（来自爱尔兰，卢森堡和意大利）的多语言法律词组中学到的浅层神经网络嵌入模型，以制定无监督的语义相似性系统来识别换位。我们评估这些模型，并将其结果与先前无人监督的方法进行比较43指令的多语言测试语料库及其相应的NIM。我们还开发监督机器学习模型以识别转换，并将其性能与不同的特征集进行比较。

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  • 来源
    《Artificial Intelligence and Law》 |2019年第2期|199-225|共27页
  • 作者

    Rohan Nanda; Giovanni Siragusa; Luigi Di Caro; Guido Boella; Lorenzo Grossio; Marco Gerbaudo; Francesco Costamagna;

  • 作者单位

    Department of Computer Science University of Turin Corso Svizzera 185 10149 Turin Italy;

    Department of Computer Science University of Turin Corso Svizzera 185 10149 Turin Italy;

    Department of Computer Science University of Turin Corso Svizzera 185 10149 Turin Italy;

    Department of Computer Science University of Turin Corso Svizzera 185 10149 Turin Italy;

    Department of Law University of Turin Lungo Dora Siena 100/A 10153 Turin Italy;

    Department of Law University of Turin Lungo Dora Siena 100/A 10153 Turin Italy;

    Department of Law University of Turin Lungo Dora Siena 100/A 10153 Turin Italy;

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  • 正文语种 eng
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  • 关键词

    Text similarity; Transposition; Machine learning;

    机译：文字相似;换位;机器学习;

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