Improving the representation of legal case texts with information extraction methods

Brüninghaus, Stefanie; Ashley, Kevin D.

doi:10.1145/383535.383540

Cited by 40 publications

(26 citation statements)

References 16 publications

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“…Interestingly, if we consider 2 nearest neighbors (the two most voted classes) rather than only the first one, the success rate raises to 88.71% for the BCN approach, and 80.65% for the NCD approach, respectively. By 6 The results obtained with no support document: AMDL 54.29% accuracy; BCN 74.29% accuracy, and NCD 61.43% accuracy. considering the three most voted classes, we obtain 97.14% correct results with BCN and 87.14% with NCD.…”

Section: Resultsmentioning

confidence: 94%

“…In a subsequent work the same authors enriched obtained enhanced categorization accuracy by providing the learning algorithms with a legal thesaurus and text parsing information [5]. In 2001 the model was extended by adding further elements, such as accounting for negation and roles, based on the observation that the presence of proper names may prevent classifiers from correctly categorizing texts (vice versa, the information on roles supports correct classification) [6]. Subsequently, the authors proposed three different sorts of representation: bag of words, with the mentioned roles, and also with "propositional patterns" including roles information.…”

Section: Related Workmentioning

confidence: 99%

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Legal documents categorization by compression

Mastropaolo

Pallante²,

Radicioni³

2013

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law

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In this paper we investigate how to categorize text excerpts from Italian normative texts. Although text categorization is a problem of broader interest, we single out a specific issue. Namely, we are concerned with categorizing the set of subjects in which Italian Regions are allowed to produce norms: this is the so-called residual legislative power problem. It basically consists in making explicit a set of subjects that was originally defined only in a residual and negative fashion. The categorization of legal text fragments is acknowledged to be a difficult problem, featured by abstract concepts along with a variety of locutions used to denote them, by convoluted sentence structure, and by several other facets. In addition, in the present case subjects are often partially overlapped, and a training set of sufficient size (for the problem under consideration) does not exist: all these aspects make our task challenging. In this setting, classical feature-based approaches provide poor quality results, so we explored algorithms based on compression techniques. We tested three such techniques: we illustrate their main features and report the results of an experimentation where our implementation of such algorithms is compared with the output of standard machine learning algorithms. Far from having found a silver bullet, we show that compression-based techniques provide the best results for the problem at hand, and argue that these approaches can be effectively coupled with more informative and semantically grounded ones.

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Section: Resultsmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

Legal documents categorization by compression

Mastropaolo

Pallante²,

Radicioni³

2013

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law

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“…The abstraction [33], representation [5,14], classification [44,50] and retrieval [1] of case laws are widely studied. Earlier research focused on building expert system for law [46,52].…”

Section: Related Workmentioning

confidence: 99%

Legal information retrieval and application to e-rulemaking

Lau

Law

Wiederhold

2005

Proceedings of the 10th International Conference on Artificial Intelligence and Law

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The complexity and diversity of government regulations make understanding the regulations a non-trivial task. One of the issues is the existence of multiple sources of regulations and interpretive guides; the latter are often independent of governing bodies. This work aims to develop an information infrastructure for legal information retrieval with applications to electronic-rulemaking. The pilot study focuses on accessibility regulations from the US Federal government, private organizations and European agencies. A shallow parser is developed to consolidate different regulations into a unified XML format, which is well suited for handling semi-structured data such as legal documents. Handcrafted rules and a text mining tool are developed to extract the important features, such as concepts, measurements, effective dates and so on, and to incorporate them into the corpus.To compare and locate related provisions from different regulatory documents, we employ Information Retrieval techniques to combine generic features with domain knowledge. Structural information from regulations, such as the hierarchical organization of provisions and heavy referencing among provisions, are used to help improve the relatedness analysis. Results are obtained to illustrate the use of regulatory structure and domain knowledge in provision comparisons. Application to an e-rulemaking scenario for a rights-of-way drafted regulation is shown to demonstrate extended capabilities of the prototype system.

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“…Ideally regulations should be readily retrievable by interested individuals. To aid understanding of the law, much prior research focused on the abstraction and retrieval of case law [1,3,5,24], analysis of regulations [15,16], and compliance guidance for regulations [12,13]. Methodologies and tools that enable the browsing of regulations according to industry-specific taxonomies are relatively lacking.…”

Section: Introductionmentioning

confidence: 99%

Mapping regulations to industry-specific taxonomies

Cheng

Lau

Law

2007

Proceedings of the 11th International Conference on Artificial Intelligence and Law

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For each industry, there exist many taxonomies that are intended for various applications. There are also multiple sources of regulations from different government agencies. Industry practitioners, unlike legal practitioners, are familiar with one or more industry-specific taxonomies but not necessarily regulatory organization systems. To help browsing of regulations by industry practitioners, we propose to map regulations to existing industry-specific taxonomies.A mapping from a single taxonomy to a single regulation is a trivial keyword matching task.From there, we examine techniques to map a single taxonomy to multiple regulations, as well as to map multiple taxonomies to a single regulation. Cosine similarity, Jaccard coefficient and market-basket analysis are tested to model the similarity metric between concepts from different taxonomies. Preliminary evaluations of the three metrics are performed. Examples from the building industry are drawn to illustrate the betterment of regulatory usage from the mapping between various taxonomies and regulations.

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Improving the representation of legal case texts with information extraction methods

Cited by 40 publications

References 16 publications

Legal documents categorization by compression

Legal documents categorization by compression

Legal information retrieval and application to e-rulemaking

Mapping regulations to industry-specific taxonomies

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