The nature of statistical learning theory
The nature of statistical learning theory
A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
The split-up system: integrating neural networks and rule-based reasoning in the legal domain
ICAIL '95 Proceedings of the 5th international conference on Artificial intelligence and law
Communications of the ACM
The effectiveness of machine learning techniques for predicting time to case disposition
Proceedings of the 6th international conference on Artificial intelligence and law
Finding factors: learning to classify case opinions under abstract fact categories
Proceedings of the 6th international conference on Artificial intelligence and law
Making large-scale support vector machine learning practical
Advances in kernel methods
A learning technique for legal document analysis
ICAIL '99 Proceedings of the 7th international conference on Artificial intelligence and law
Improving the representation of legal case texts with information extraction methods
Proceedings of the 8th international conference on Artificial intelligence and law
Automatic categorization of case law
Proceedings of the 8th international conference on Artificial intelligence and law
Automatic text representation, classification and labeling in European law
Proceedings of the 8th international conference on Artificial intelligence and law
A machine learning approach to prior case retrieval
Proceedings of the 8th international conference on Artificial intelligence and law
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Feature Selection for Unbalanced Class Distribution and Naive Bayes
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Predicting outcomes of case based legal arguments
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Classification and clustering for case-based criminal summary judgments
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
MITRE: description of the Alembic system used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
Is linguistic information relevant for the classification of legal texts?
ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
A question-answering system for Portuguese juridical documents
ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
A Question Answer System for Legal Information Retrieval
Proceedings of the 2005 conference on Legal Knowledge and Information Systems: JURIX 2005: The Eighteenth Annual Conference
Knowledge Discovery from Legal Databases
Knowledge Discovery from Legal Databases
Named entity recognition and resolution in legal text
Semantic Processing of Legal Texts
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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.