Diversifying the Legal Order [chapter]

Marios Koniaris, Ioannis Anagnostopoulos, Yannis Vassiliou
2016 IFIP Advances in Information and Communication Technology  
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
more » ... 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 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 data set from the Common Law domain that we subjectively 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. Data Set: https://github.com/mkoniari/LegalDivEval Many network-based ranking approaches have been proposed to rank objects according to different criteria [24] and recently diversification of the results has attracted attention. Research is currently focused on two directions: a greedy vertex selection procedure and a vertex reinforced random walk. The greedy vertex selection procedure, at each iteration, selects and removes from the graph the vertex with maximum random walk based ranking score. One of the earlier algorithms that address diversified ranking on graphs by vertex selection with absorbing random walks is Grasshopper [5] . A diversity-focused ranking methodology, based on reinforced random walks, was introduced in [4]. Their proposed model, DivRank, incorporates the rich-gets-richer mechanism to PageRank [25] with reinforcements on transition probabilities between vertices. We utilize these approaches in our diversification framework considering the connectivity matrix of the citation network between documents that are relevant for a given user query. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted:
doi:10.1007/978-3-319-44944-9_44 fatcat:gjqpobvhxfcw5dgnexscpfc7na