在多司法管辖区的背景下,转移法律文本分类的预测模型

Jaromír Šavelka, Kevin D. Ashley
{"title":"在多司法管辖区的背景下,转移法律文本分类的预测模型","authors":"Jaromír Šavelka, Kevin D. Ashley","doi":"10.1145/2746090.2746109","DOIUrl":null,"url":null,"abstract":"In this paper we use statistical machine learning to classify statutory texts in terms of highly specific functional categories. We focus on regulatory provisions from multiple US state jurisdictions, all dealing with the same general topic of public health system emergency preparedness and response. In prior work we have established that one can improve classification performance on one jurisdiction's statutory texts using texts from another jurisdiction. Here we describe a framework facilitating transfer of predictive models for classification of statutory texts among multiple state jurisdictions. Our results show that the classification performance improves as we employ an increasing number of models trained on data coming from different states.","PeriodicalId":309125,"journal":{"name":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Transfer of predictive models for classification of statutory texts in multi-jurisdictional settings\",\"authors\":\"Jaromír Šavelka, Kevin D. Ashley\",\"doi\":\"10.1145/2746090.2746109\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we use statistical machine learning to classify statutory texts in terms of highly specific functional categories. We focus on regulatory provisions from multiple US state jurisdictions, all dealing with the same general topic of public health system emergency preparedness and response. In prior work we have established that one can improve classification performance on one jurisdiction's statutory texts using texts from another jurisdiction. Here we describe a framework facilitating transfer of predictive models for classification of statutory texts among multiple state jurisdictions. Our results show that the classification performance improves as we employ an increasing number of models trained on data coming from different states.\",\"PeriodicalId\":309125,\"journal\":{\"name\":\"Proceedings of the 15th International Conference on Artificial Intelligence and Law\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 15th International Conference on Artificial Intelligence and Law\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2746090.2746109\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2746090.2746109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

在本文中,我们使用统计机器学习根据高度具体的功能类别对法律文本进行分类。我们重点关注美国多个州司法管辖区的监管规定,所有这些规定都涉及公共卫生系统应急准备和响应的相同主题。在之前的工作中,我们已经确定可以使用来自另一个司法管辖区的文本来提高对一个司法管辖区的法定文本的分类性能。在这里,我们描述了一个框架,促进了在多个州管辖范围内对法律文本分类的预测模型的转移。我们的结果表明,随着我们使用越来越多的模型来训练来自不同状态的数据,分类性能得到了提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transfer of predictive models for classification of statutory texts in multi-jurisdictional settings
In this paper we use statistical machine learning to classify statutory texts in terms of highly specific functional categories. We focus on regulatory provisions from multiple US state jurisdictions, all dealing with the same general topic of public health system emergency preparedness and response. In prior work we have established that one can improve classification performance on one jurisdiction's statutory texts using texts from another jurisdiction. Here we describe a framework facilitating transfer of predictive models for classification of statutory texts among multiple state jurisdictions. Our results show that the classification performance improves as we employ an increasing number of models trained on data coming from different states.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信