Automatic categorization of case law

Thompson, Paul

doi:10.1145/383535.383543

Cited by 38 publications

(20 citation statements)

References 22 publications

(28 reference statements)

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“…The work by [29] investigates how to automate the indexing of the West Legal Directory, an online legal retrieval system [1]. In particular, the paper provides a comparison of classification results obtained with a C4.5, with kNN using TF-IDF as distance measure and with Ripper, a rule induction algorithm.…”

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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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: 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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“…- [39] used decision trees to extract rules to estimate the number of days until the final case disposition; - [40] developed rule based and neural networks legal systems; - [7] used neural networks to model legal classifiers; - [14,15] used SVM to classify juridical Portuguese documents; - [34] proposed a framework for the automatic categorisation of case laws; - [30,31] described the use of self-organising maps (SOM) to obtain clusters of legal documents in an information retrieval environment and explored the problem of text classification in the context of the European law; - [23] described classification and clustering approaches to case-based criminal summaries; -[9, 8, 10] described also related work using linear classifiers for documents; - [2] integrated information extraction, information retrieval and machine learning techniques in order to design a case-based retrieval system able to find prior relevant cases. They used SVMs to rank prior case candidates.…”

Section: Related Workmentioning

confidence: 99%

Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents

Quaresma

Gonçalves

2010

Lecture Notes in Computer Science

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Abstract. Information extraction from legal documents is an important and open problem. A mixed approach, using linguistic information and machine learning techniques, is described in this paper. In this approach, top-level legal concepts are identified and used for document classification using Support Vector Machines. Named entities, such as, locations, organizations, dates, and document references, are identified using semantic information from the output of a natural language parser. This information, legal concepts and named entities, may be used to populate a simple ontology, allowing the enrichment of documents and the creation of high-level legal information retrieval systems. The proposed methodology was applied to a corpus of legal documents -from the EUR-Lex site -and it was evaluated. The obtained results were quite good and indicate this may be a promising approach to the legal information extraction problem.

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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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Automatic categorization of case law

Cited by 38 publications

References 22 publications

Legal documents categorization by compression

Legal documents categorization by compression

Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents

Legal information retrieval and application to e-rulemaking

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