Proceedings of the 15th International Conference on Artificial Intelligence and Law最新文献

筛选

英文中文

How to ground a language for legal discourse in a prototypical perceptual semantics 如何在一个典型的感性语义学中建立法律话语的语言基础

Proceedings of the 15th International Conference on Artificial Intelligence and Law Pub Date : 2015-06-08 DOI: 10.1145/2746090.2746091

L. McCarty

引用次数: 3

Machine learning for readability of legislative sentences 立法句子可读性的机器学习

Proceedings of the 15th International Conference on Artificial Intelligence and Law Pub Date : 2015-06-08 DOI: 10.1145/2746090.2746095

Michael Curtotti, Eric C. McCreath, Tom Bruce, Sara S. Frug, W. Weibel, Nicolas Ceynowa

{"title":"Machine learning for readability of legislative sentences","authors":"Michael Curtotti, Eric C. McCreath, Tom Bruce, Sara S. Frug, W. Weibel, Nicolas Ceynowa","doi":"10.1145/2746090.2746095","DOIUrl":"https://doi.org/10.1145/2746090.2746095","url":null,"abstract":"Improving the readability of legislation is an important and unresolved problem. Recently, researchers have begun to apply legal informatics to this problem. This paper applies machine learning to predict the readability of sentences from legislation and regulations. A corpus of sentences from the United States Code and US Code of Federal Regulations was created. Each sentence was labelled for language difficulty using results from a large-scale crowdsourced study undertaken during 2014. The corpus was used as training and test data for machine learning. The corpus includes a version tagged using the Stanford parser context free grammar and a version tagged using the Stanford dependency grammar parser. The corpus is described and made available to interested researchers. We investigated whether extending natural language features available as input to machine learning improves the accuracy of prediction. Among features evaluated are those from the context free and dependency grammars. Letter and word ngrams were also studied. We found the addition of such features improves accuracy of prediction on legal language. We also undertake a correlation study of natural language features and language difficulty drawing insights as to the characteristics that may make legal language more difficult. These insights, and those from machine learning, enable us to describe a system for reducing legal language difficulty and to identify a number of suggested heuristics for improving the writing of legislation and regulations.","PeriodicalId":309125,"journal":{"name":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132962500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Thou shalt is not you will 你要做的不是你要做的

Proceedings of the 15th International Conference on Artificial Intelligence and Law Pub Date : 2014-04-07 DOI: 10.1145/2746090.2746105

Guido Governatori

引用次数: 51

首页上一页