David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
Artificial Intelligence and Law 9 (1):29-57 (2001)
Legal text retrieval traditionally relies upon external knowledge sources such as thesauri and classification schemes, and an accurate indexing of the documents is often manually done. As a result not all legal documents can be effectively retrieved. However a number of current artificial intelligence techniques are promising for legal text retrieval. They sustain the acquisition of knowledge and the knowledge-rich processing of the content of document texts and information need, and of their matching. Currently, techniques for learning information needs, learning concept attributes of texts, information extraction, text classification and clustering, and text summarization need to be studied in legal text retrieval because of their potential for improving retrieval and decreasing the cost of manual indexing. The resulting query and text representations are semantically much richer than a set of key terms. Their use allows for more refined retrieval models in which some reasoning can be applied. This paper gives an overview of the state of the art of these innovativetechniques and their potential for legal text retrieval.
|Keywords||case retrieval model information discovery legal text retrieval machine learning|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
Ephraim Nissan (forthcoming). Digital Technologies and Artificial Intelligence’s Present and Foreseeable Impact on Lawyering, Judging, Policing and Law Enforcement. AI and Society.
Soufiane El Jelali, Elisabetta Fersini & Enza Messina (2015). Legal Retrieval as Support to eMediation: Matching Disputant’s Case and Court Decisions. Artificial Intelligence and Law 23 (1):1-22.
Similar books and articles
Adel Saadoun, Jean-Louis Ermine, Claude Belair & Jean-Mark Pouyot (1997). A Knowledge Engineering Framework for Intelligent Retrieval of Legal Case Studies. Artificial Intelligence and Law 5 (3):179-205.
Luuk Matthijssen (1998). A Task-Based Interface to Legal Databases. Artificial Intelligence and Law 6 (1):81-103.
Dieter Merkl, Erich Schweighoffer & Werner Winiwarter (1999). Exploratory Analysis of Concept and Document Spaces with Connectionist Networks. Artificial Intelligence and Law 7 (2-3):185-209.
Guiraude Lame (2004). Using NLP Techniques to Identify Legal Ontology Components: Concepts and Relations. [REVIEW] Artificial Intelligence and Law 12 (4):379-396.
José Saias & Paulo Quaresma (2004). A Methodology to Create Legal Ontologies in a Logic Programming Based Web Information Retrieval System. Artificial Intelligence and Law 12 (4):397-417.
Wim Peters, Maria-Teresa Sagri & Daniela Tiscornia (2007). The Structuring of Legal Knowledge in Lois. Artificial Intelligence and Law 15 (2):117-135.
Gian Piero Zarri (2007). Ontologies and Reasoning Techniques for (Legal) Intelligent Information Retrieval Systems. Artificial Intelligence and Law 15 (3):251-279.
M. Saravanan, B. Ravindran & S. Raman (2009). Improving Legal Information Retrieval Using an Ontological Framework. Artificial Intelligence and Law 17 (2):101-124.
Howard Turtle (1995). Text Retrieval in the Legal World. Artificial Intelligence and Law 3 (1-2):5-54.
Added to index2009-01-28
Total downloads11 ( #312,966 of 1,907,886 )
Recent downloads (6 months)3 ( #272,049 of 1,907,886 )
How can I increase my downloads?