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  1. Distributivity, Collectivity, and Cumulativity in Terms of (In)dependence and Maximality.Livio Robaldo - 2011 - Journal of Logic, Language and Information 20 (2):233-271.
    This article proposes a new logical framework for NL quantification. The framework is based on Generalized Quantifiers, Skolem-like functional dependencies, and Maximality of the involved sets of entities. Among the readings available for NL sentences, those where two or more sets of entities are independent of one another are particularly challenging. In the literature, examples of those readings are known as Collective and Cumulative readings. This article briefly analyzes previous approaches to Cumulativity and Collectivity, and indicates (Schwarzschild in Pluralities. Kluwer, (...)
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  • The European Legal Taxonomy Syllabus: A multi-lingual, multi-level ontology framework to untangle the web of European legal terminology.Gianmaria Ajani, Guido Boella, Luigi di Caro, Livio Robaldo, Llio Humphreys, Sabrina Praduroux, Piercarlo Rossi & Andrea Violato - 2016 - Applied ontology 11 (4):325-375.
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  • Automated reference resolution in legal texts.Oanh Thi Tran, Bach Xuan Ngo, Minh Le Nguyen & Akira Shimazu - 2014 - Artificial Intelligence and Law 22 (1):29-60.
    This paper investigates the task of reference resolution in the legal domain. This is a new interesting task in Legal Engineering research. The goal is to create a system which can automatically detect references and then extracts their referents. Previous work limits itself to detect and resolve references at the document targets. In this paper, we go a step further in trying to resolve references to sub-document targets. Referents extracted are the smallest fragments of texts in documents, rather than the (...)
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  • TULSI: an NLP system for extracting legal modificatory provisions. [REVIEW]Leonardo Lesmo, Alessandro Mazzei, Monica Palmirani & Daniele P. Radicioni - 2013 - Artificial Intelligence and Law 21 (2):139-172.
    In this work we present the TULSI system (so named after Turin University Legal Semantic Interpreter), a system to produce automatic annotations of normative documents through the extraction of modificatory provisions. TULSI relies on a deep syntactic analysis and a shallow semantic interpreter that are illustrated in detail. We report the results of an experimental evaluation of the system and discuss them, also suggesting future directions for further improvement.
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