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Legal NERC with ontologies, Wikipedia and curriculum learning.

, , , and . EACL (2), page 254-259. Association for Computational Linguistics, (2017)

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Learning Slowly To Learn Better: Curriculum Learning for Legal Ontology Population., , , and . FLAIRS, page 252-257. AAAI Press, (2017)You can simply rely on communities for a robust characterization of stances., , , , and . FLAIRS, (2021)Ontology Population and Alignment for the Legal Domain: YAGO, Wikipedia and LKIF., , , and . ISWC (Posters, Demos & Industry Tracks), volume 1963 of CEUR Workshop Proceedings, CEUR-WS.org, (2017)Licentia: a Tool for Supporting Users in Data Licensing on the Web of Data., , , , , and . ISWC (Posters & Demos), volume 1272 of CEUR Workshop Proceedings, page 277-280. CEUR-WS.org, (2014)Legal NERC with ontologies, Wikipedia and curriculum learning., , , and . EACL (2), page 254-259. Association for Computational Linguistics, (2017)Increasing Argument Annotation Reproducibility by Using Inter-annotator Agreement to Improve Guidelines., , , , and . LREC, European Language Resources Association (ELRA), (2018)A low-cost, high-coverage legal named entity recognizer, classifier and linker., , , and . ICAIL, page 9-18. ACM, (2017)A Study on Multiple Tasks for e-Commerce Marketplaces., and . FLAIRS, (2021)A Study on Title Encoding Methods for e-Commerce Downstream Tasks., and . FLAIRS, (2022)Convolutional Ladder Networks for Legal NERC and the Impact of Unsupervised Data in Better Generalizations., , , , and . FLAIRS, page 155-160. AAAI Press, (2019)