Michael Curtotti, Eric C. McCreath, Tom Bruce, Sara S. Frug, W. Weibel, Nicolas Ceynowa
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.
{"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}
The paper examines the citation network of the via incidentale rulings of the Italian Constitutional Court ("ICC"), vis-à-vis the web of scholarly opinions, comments, and annotations, devoted to such cases. The aim is to deepen the notion of legal relevance. On the one hand, a remarkable number of cases that are considerably discussed by experts, are neither hubs nor authorities in the ICC citation network. On the other hand, cases that are relevant in the ICC citation network are scarcely debated, or even ignored, by scholars. This twofold outcome suggests that we should combine research on the citation network of the courts with the web of scholarly opinions, to obtain a more detailed picture of which decisions and verdicts have to be reckoned as relevant in a given legal system.
{"title":"The case law of the Italian constitutional court, its power laws, and the web of scholarly opinions","authors":"T. Agnoloni, U. Pagallo","doi":"10.1145/2746090.2746108","DOIUrl":"https://doi.org/10.1145/2746090.2746108","url":null,"abstract":"The paper examines the citation network of the via incidentale rulings of the Italian Constitutional Court (\"ICC\"), vis-à-vis the web of scholarly opinions, comments, and annotations, devoted to such cases. The aim is to deepen the notion of legal relevance. On the one hand, a remarkable number of cases that are considerably discussed by experts, are neither hubs nor authorities in the ICC citation network. On the other hand, cases that are relevant in the ICC citation network are scarcely debated, or even ignored, by scholars. This twofold outcome suggests that we should combine research on the citation network of the courts with the web of scholarly opinions, to obtain a more detailed picture of which decisions and verdicts have to be reckoned as relevant in a given legal system.","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":"131901020","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}
In this paper we discuss some reasons why temporal logic might not be suitable to model real life norms. To show this, we present a novel deontic logic contrary-to-duty/derived permission paradox based on the interaction of obligations, permissions and contrary-to-duty obligations. The paradox is inspired by real life norms.
{"title":"Thou shalt is not you will","authors":"Guido Governatori","doi":"10.1145/2746090.2746105","DOIUrl":"https://doi.org/10.1145/2746090.2746105","url":null,"abstract":"In this paper we discuss some reasons why temporal logic might not be suitable to model real life norms. To show this, we present a novel deontic logic contrary-to-duty/derived permission paradox based on the interaction of obligations, permissions and contrary-to-duty obligations. The paradox is inspired by real life norms.","PeriodicalId":309125,"journal":{"name":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127576353","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}
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