Abstract
The use of pragmatics in applying the law is hard to deal with for a legal knowledge engineer who needs to model it in a precise KR for (semi-)automated legal reasoning systems. The negative aspects of pragmatics is due to the difficulty involved in separating their concerns. When representing a legal norm for (semi-)automated reasoning, an important step/aspect is the annotation of legal sections under consideration. Annotation in the context of this paper refers to identification, segregation and thereafter representation of the content and its associated context. In this paper we present an approach and provide a proof-of-concept implementation for automatizing the process of identifying the most relevant judgment pertaining to a legal section and further transforming them into a formal representation format. The annotated legal section and its related judgments can then be mapped into a decision model for further down the line processing.
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The textual content inside the decision model is left out on purpose to handle the space restrictions.
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Represented using green color.
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While the authors understand that an ideal approach is to use a expert driven gold standard construction, a common consensus on thus generated standards for relevance is debatable in the context of case-laws.
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Ramakrishna, S., Górski, Ł., Paschke, A. (2018). KR4IPLaw Judgment Miner - Case-Law Mining for Legal Norm Annotation. In: Pagallo, U., Palmirani, M., Casanovas, P., Sartor, G., Villata, S. (eds) AI Approaches to the Complexity of Legal Systems. AICOL AICOL AICOL AICOL AICOL 2015 2016 2016 2017 2017. Lecture Notes in Computer Science(), vol 10791. Springer, Cham. https://doi.org/10.1007/978-3-030-00178-0_22
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