Evaluation of Diversification Techniques for Legal Information Retrieval†
Abstract“Public legal information from all countries and international institutions is part of the common heritage of humanity. Maximizing access to this information promotes justice and the rule of law”. In accordance with the aforementioned declaration on free access to law by legal information institutes of the world, a plethora of legal information is available through the Internet, while the provision of legal information has never before been easier. Given that law is accessed by a much wider group of people, the majority of whom are not legally trained or qualified, diversification techniques should be employed in the context of legal information retrieval, as to increase user satisfaction. We address the diversification of results in legal search by adopting several state of the art methods from the web search, network analysis and text summarization domains. We provide an exhaustive evaluation of the methods, using a standard dataset from the common law domain that we objectively annotated with relevance judgments for this purpose. Our results: (i) reveal that users receive broader insights across the results they get from a legal information retrieval system; (ii) demonstrate that web search diversification techniques outperform other approaches (e.g., summarization-based, graph-based methods) in the context of legal diversification; and (iii) offer balance boundaries between reinforcing relevant documents or sampling the information space around the legal query. View Full-Text
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Koniaris, M.; Anagnostopoulos, I.; Vassiliou, Y. Evaluation of Diversification Techniques for Legal Information Retrieval. Algorithms 2017, 10, 22.
Koniaris M, Anagnostopoulos I, Vassiliou Y. Evaluation of Diversification Techniques for Legal Information Retrieval. Algorithms. 2017; 10(1):22.Chicago/Turabian Style
Koniaris, Marios; Anagnostopoulos, Ioannis; Vassiliou, Yannis. 2017. "Evaluation of Diversification Techniques for Legal Information Retrieval." Algorithms 10, no. 1: 22.
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