Functional Text Segmentation is the task of partitioning a textual document in segments that play a certain function. In the legal domain, this is important to support downstream tasks, but it faces also challenges of segment discontinuity, few-shot scenario, and domain specificity. We propose an approach that, revisiting the underlying graph structure of a Conditional Random Field and relying on a combination of neural embeddings and engineered features, is capable of addressing these challenges. Evaluation on a dataset of Italian case law decisions yields promising results.
Few-Shot Legal Text Segmentation via Rewiring Conditional Random Fields: A Preliminary Study / A. Ferrara, S. Picascia, D. Riva (LECTURE NOTES IN COMPUTER SCIENCE). - In: Advances in Conceptual Modeling / [a cura di] T.P. Sales, J. Araújo, J. Borbinha, G. Guizzardi. - Cham : Springer, 2023. - ISBN 9783031471117. - pp. 141-150 (( Intervento presentato al 42. convegno ER 2023 Workshops, CMLS, CMOMM4FAIR, EmpER, JUSMOD, OntoCom, QUAMES, and SmartFood tenutosi a Lisboa nel 2023 [10.1007/978-3-031-47112-4_13].
Few-Shot Legal Text Segmentation via Rewiring Conditional Random Fields: A Preliminary Study
A. FerraraPrimo
;S. PicasciaSecondo
;D. Riva
Ultimo
2023
Abstract
Functional Text Segmentation is the task of partitioning a textual document in segments that play a certain function. In the legal domain, this is important to support downstream tasks, but it faces also challenges of segment discontinuity, few-shot scenario, and domain specificity. We propose an approach that, revisiting the underlying graph structure of a Conditional Random Field and relying on a combination of neural embeddings and engineered features, is capable of addressing these challenges. Evaluation on a dataset of Italian case law decisions yields promising results.File | Dimensione | Formato | |
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