We present a system for mapping facts and knowledge in legal texts, in particular case law opinions and holdings to knowledge graphs, enabling advanced semantic search over the case law corpus, as well as matching of case descriptions onto case laws using graph similarity. The essential components for knowledge graph generations are deep linguistic NLP components. We discuss how the deep analyses provided by these components allow us to process not only the core semantic relations in the legal documents, but also to process advanced semantic and pragmatic properties, including implicatures and presuppositions.
@InProceedings{CAVAR18.7, author = {Damir Cavar ,Joshua Herring and Anthony Meyer}, title = {Case Law Analysis using Deep NLP and Knowledge Graphs}, booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {may}, date = {7-12}, location = {Miyazaki, Japan}, editor = {Georg Rehm and Víctor Rodríguez-Doncel and Julián Moreno-Schneider}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {979-10-95546-18-4}, language = {english} }
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