A Low-cost, High-coverage Legal Named Entity Recognizer, Classifier and Linker

Cristian Cardellino 1 Milagro Teruel 1 Laura Alonso Alemany 1 Serena Villata 2
2 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : In this paper, we try to improve Information Extraction in legal texts by creating a legal Named Entity Recognizer, Classifier and Linker. With this tool, we can identify relevant parts of texts and connect them to a structured knowledge representation, the LKIF ontology. More interestingly, this tool has been developed with relatively little effort, by mapping the LKIF ontology to the YAGO ontology and through it, taking advantage of the mentions of entities in the Wikipedia. These mentions are used as manually annotated examples to train the Named Entity Recognizer, Classifier and Linker. We have evaluated the approach on holdout texts from the Wikipedia and also on a small sample of judgments of the European Court of Human Rights, resulting in a very good performance, i.e., around 80% F-measure for different levels of granularity. We present an extensive error analysis to direct further developments, and we expect that this approach can be successfully ported to other legal subdomains, represented by different ontologies.
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Cristian Cardellino, Milagro Teruel, Laura Alonso Alemany, Serena Villata. A Low-cost, High-coverage Legal Named Entity Recognizer, Classifier and Linker . ICAIL-2017 - 16th International Conference on Artificial Intelligence and Law, Jun 2017, Londres, United Kingdom. pp.22. ⟨hal-01541446⟩

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