Convolutional Ladder Networks for Legal {NERC} and the Impact of Unsupervised Data in Better Generalizations

Cristian Cardellino 1 Laura Alonso Alemany 1 Milagro Teruel 1 Serena Villata 2 Santiago Marro
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 adapt the semi-supervised deep learning architecture known as "Convolutional Ladder Networks", from the domain of computer vision, and explore how well it works for a semi-supervised Named Entity Recognition and Classification task with legal data. The idea of exploring a semi-supervised technique is to assess the impact of large amounts of unsupervised data (cheap to obtain) in specific tasks that have little annotated data, in order to develop robust models that are less prone to overfitting. In order to achieve this, first we must check the impact on a task that is easier to measure. We are presenting some preliminary experiments, however, the results obtained foster further research in the topic.
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Cristian Cardellino, Laura Alonso Alemany, Milagro Teruel, Serena Villata, Santiago Marro. Convolutional Ladder Networks for Legal {NERC} and the Impact of Unsupervised Data in Better Generalizations. Proceedings of the Thirty-Second International Florida Artificial Intelligence Research Society Conference, May 2019, Sarasota, United States. ⟨hal-02381093⟩

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