Automatic Consolidation of Japanese Statutes Based on Formalization of Amendment Sentences

Ogawa, Yasushi; Inagaki, Shintaro; Toyama, Katsuhiko

doi:10.1007/978-3-540-78197-4_34

Cited by 23 publications

(8 citation statements)

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“…A recent work on the topic of automatic consolidation has been proposed in [21]. This work builds on patternmatching, and performs a basic XML preprocessing; no sort of parsing information is used.…”

Section: Related Workmentioning

confidence: 99%

Modificatory provisions detection

Gianfelice¹,

Lesmo²,

Palmirani

et al. 2013

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law

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In the last few years University of Turin and CIRSFID University of Bologna collaborated to pair NLP techniques and legal knowledge to detect modificatory provisions in normative texts. Annotating these modifications is a relevant and interesting problem, in that modifications affect the whole normative system; and legal language, though more regular than unrestricted language, is sometimes particularly convoluted, and poses specific linguistic issues. This paper focuses on two major aspects. First, we explore a combination between parsing and regular expressions; to the best of our knowledge, such hybrid strategy has never been proposed before to tackle the problem at hand. Secondly, we significantly extend past works coverage (basically focussed on substitution, integration and repeal modifications) in order to account for further twelve modification kinds. For the sake of conciseness, we fully illustrate and discuss only few modification types that are more relevant and interesting: suspension, prorogation of efficacy, postponement of efficacy and exception/derogation. These sorts of modifications appear particularly challenging, in that modifications in these categories make use of similar linguistic speech acts and verbs, and exhibit strong similarities in the linguistic syntactical patterns, to such an extent that to discern them is difficult for the legal expert, too. We describe the implemented system and report about an extensive experimentation on the new modificatory provisions. Results are discussed in order to improve both system's accuracy and

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Section: Related Workmentioning

confidence: 99%

Modificatory provisions detection

Gianfelice¹,

Lesmo²,

Palmirani

et al. 2013

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law

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show abstract

“…26 In the last case, correctly identifying the modification affected the precision but not the recall of the system's results. …”

Section: On Preprocessing and Annotation Errorsmentioning

confidence: 98%

“…A recent work on the topic of automatic consolidation has been proposed in [26]. This work proposes a pattern-matching based approach analysing text that uses a basic XML preprocessing, but without using a parser.…”

Section: Related Workmentioning

confidence: 99%

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TULSI: an NLP system for extracting legal modificatory provisions

et al. 2012

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In this work we present the TULSI system (so named after Turin University Legal Semantic Interpreter), a system to produce automatic annotations of normative documents through the extraction of modificatory provisions. TULSI relies on a deep syntactic analysis and a shallow semantic interpreter that are illustrated in detail. We report the results of an experimental evaluation of the system and discuss them, also suggesting future directions for further improvement.

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“…Therefore, in the following sections we show analysis of law sentences and explain our methodology, which is based on the previous study by Ogawa et al [10], who proposed a method for rewriting texts using regular expressions in order to consolidate legal sentences and amendment sentences.…”

Section: Problems Of Wildcatsmentioning

confidence: 99%

Treatment of Legal Sentences Including Itemized and Referential Expressions – Towards Translation into Logical Forms

Kimura

Nakamura

Shimazu

2009

New Frontiers in Artificial Intelligence

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Abstract. This paper proposes a framework for analyzing legal sentences including itemized or referential expressions. Thus far, we have developed a system for translating legal documents into logical formulae. Although our system basically converts words and phrases in a target sentence into predicates in a logical formula, it generates some useless predicates for itemized and referential expressions. We propose a front end system which substitutes corresponding referent phrases for these expressions. Thus, the proposed system generates a meaningful text with high readability, which can be input into our translation system. We examine our system with actual data of legal documents. As a result, the system was 73.1% accurate in terms of removing itemized expressions in a closed test, and 51.4% accurate in an open test.

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Automatic Consolidation of Japanese Statutes Based on Formalization of Amendment Sentences

Cited by 23 publications

References 1 publication

Modificatory provisions detection

Modificatory provisions detection

TULSI: an NLP system for extracting legal modificatory provisions

Treatment of Legal Sentences Including Itemized and Referential Expressions – Towards Translation into Logical Forms

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