LARC: Learning to Assign Knowledge Roles to Textual Cases

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In this paper, we present a learning framework for the semantic annotation of text documents that can be used as textual cases in case-based reasoning applications. The annotations are known as knowledge roles and are task-dependent. The framework relies on deep natural language processing techniques and does not require the existence of any domain-dependent resources. Several experiments are presented to demonstrate the feasibility of the proposed approach. The results show that the framework allows to robustly label cases with features which can be used for case representation, contributing to the retrieval of and the reasoning with textual cases.