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A lawyer directory service using legal documents and profile information as support

Published: 06 June 2005 Publication History

Abstract

This paper describes the use of case laws, dockets as well as other legal publications and attorney profile information to aid the development of a lawyer directory. In order to provide a trustworthy Internet legal research tool, we define a similarity score to represent the match between a lawyer and desired practice experience. The score quantifies the relationship between lawyers and legal experience through litigation records, such as Federal and State cases, as well as profile information, such as published articles in law journals. Relevant documents are linked to different practice areas through an automated classification tool and domain expert mappings. The resulting lawyer-experience mapping enables users to perform an advanced search for lawyers by practice experience in addition to a traditional name search.

References

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Calado, P., Ribeiro-Neto, B., Ziviani, N., Moura, E., and Silva, I. Local versus Global Link Information in the Web. ACM Trans. on Information Systems, 21, 1 (2003), 42--63.
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Gibson, D., Kleinberg, J., and Raghavan, P. Inferring Web Communities from Link Topology. In Proceedings of the 9th ACM Conference on Hypertext and Hypermedia (Pittsburgh, PA, 1998), 1998, 225--234.
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Page, L., Brin, S., Motwani, R., and Winograd, T. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report, Stanford University, Stanford, CA, 1998.
  1. A lawyer directory service using legal documents and profile information as support

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    cover image ACM Other conferences
    ICAIL '05: Proceedings of the 10th international conference on Artificial intelligence and law
    June 2005
    270 pages
    ISBN:1595930817
    DOI:10.1145/1165485
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • The International Association for Artificial Intelligence and Law
    • CIRSFID

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 06 June 2005

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