Is linguistic information relevant for the classification of legal texts?

Gonçalves, Teresa; Quaresma, Paulo

doi:10.1145/1165485.1165512

Cited by 25 publications

(19 citation statements)

References 23 publications

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“…Other programs have assigned rhetorical roles to case sentences based on manually annotated decisions [24], categorized legal cases by abstract WLN categories (e.g., finance and banking, bankruptcy) [25] or general topics (e.g., exceptional services pension, retirement) [15], and determined the role of a sentence in the case (e.g., as describing the applicable law or the facts) [16].…”

Section: Related Workmentioning

confidence: 99%

Introducing LUIMA

Grabmair

Ashley

Chen

et al. 2015

Proceedings of the 15th International Conference on Artificial Intelligence and Law

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This paper presents first results from a proof of feasibility experiment in conceptual legal document retrieval in a particular domain (involving vaccine injury compensation). The conceptual markup of documents is done automatically using LUIMA, a law-specific semantic extraction toolbox based on the UIMA framework. The system consists of modules for automatic sub-sentence level annotation, machine learning based sentence annotation, basic retrieval using Apache Lucene and a machine learning based reranking of retrieved documents. In a leave-one-out experiment on a limited corpus, the resulting rankings scored higher for most tested queries than baseline rankings created using a commercial full-text legal information system.

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Section: Related Workmentioning

confidence: 99%

Introducing LUIMA

Grabmair

Ashley

Chen

et al. 2015

Proceedings of the 15th International Conference on Artificial Intelligence and Law

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“…70-77). More recently, experimenters showed that some linguistic processing, including stemming and part-of-speech tagging helped assign general topic categories (e.g., exceptional services pension, retirement, competence) to legal cases (i.e., decisions of the Portuguese Attorney General's office) (Gonçalves and Quaresma 2005). Both sets of experiments involved automatically assigning abstract topics as categories; in the context of the work reported here, a comparably abstract category would be ''trade secret misappropriation''.…”

Section: Information Extraction From Case Textsmentioning

confidence: 99%

Automatically classifying case texts and predicting outcomes

2009

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Work on a computer program called SMILE ? IBP (SMart Index Learner Plus Issue-Based Prediction) bridges case-based reasoning and extracting information from texts. The program addresses a technologically challenging task that is also very relevant from a legal viewpoint: to extract information from textual descriptions of the facts of decided cases and apply that information to predict the outcomes of new cases. The program attempts to automatically classify textual descriptions of the facts of legal problems in terms of Factors, a set of classification concepts that capture stereotypical fact patterns that effect the strength of a legal claim, here trade secret misappropriation. Using these classifications, the program can evaluate and explain predictions about a problem's outcome given a database of previously classified cases. This paper provides an extended example illustrating both functions, prediction by IBP and text classification by SMILE, and reports empirical evaluations of each. While IBP's results are quite strong, and SMILE's much weaker, SMILE ? IBP still has some success predicting and explaining the outcomes of case scenarios input as texts. It marks the first time to our knowledge that a program can reason automatically about legal case texts.

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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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Is linguistic information relevant for the classification of legal texts?

Cited by 25 publications

References 23 publications

Introducing LUIMA

Introducing LUIMA

Automatically classifying case texts and predicting outcomes

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

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