As legal regulations evolve, companies and organizations are tasked with quickly understanding and adapting to regulation changes. Tools like legal knowledge bases can facilitate this process, by either helping users navigate legal information or become aware of potentially relevant updates. At their core, these tools require legal references from many sources to be unified, e.g., by legal entity linking. This is challenging since legal references are often implicitly expressed, or combined via a context. In this paper, we prototype a machine learning approach to link legal references and retrieve combinations for a given context, based on standard features and classifiers, as used in entity resolution. As an extension, we evaluate an enhancement of those features with topic vectors, aiming to capture the relevant context of the passage containing a reference.We experiment with a repository of authoritative sources on German law for building topic models and extracting legal references and report that topic models do indeed contribute in improving supervised entity linking and reference retrieval.
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