Citations of work:

Kevin D. Ashley & Will Bridewell (2010). Emerging AI & Law Approaches to Automating Analysis and Retrieval of Electronically Stored Information in Discovery Proceedings.

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  1.  3
    A Crowdsourcing Approach to Building a Legal Ontology From Text.Anatoly P. Getman & Volodymyr V. Karasiuk - 2014 - Artificial Intelligence and Law 22 (3):313-335.
  2.  17
    E-Discovery Revisited: The Need for Artificial Intelligence Beyond Information Retrieval. [REVIEW]Jack G. Conrad - 2010 - Artificial Intelligence and Law 18 (4):321-345.
    In this work, we provide a broad overview of the distinct stages of E-Discovery. We portray them as an interconnected, often complex workflow process, while relating them to the general Electronic Discovery Reference Model (EDRM). We start with the definition of E-Discovery. We then describe the very positive role that NIST’s Text REtrieval Conference (TREC) has added to the science of E-Discovery, in terms of the tasks involved and the evaluation of the legal discovery work performed. Given the critical nature (...)
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    Afterword: Data, Knowledge, and E-Discovery. [REVIEW]David D. Lewis - 2010 - Artificial Intelligence and Law 18 (4):481-486.
    Research in Artificial Intelligence (AI) and the Law has maintained an emphasis on knowledge representation and formal reasoning during a period when statistical, data-driven approaches have ascended to dominance within AI as a whole. Electronic discovery is a legal application area, with substantial commercial and research interest, where there are compelling arguments in favor of both empirical and knowledge-based approaches. We discuss the cases for both perspectives, as well as the opportunities for beneficial synergies.
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