Deontic modality (obligation, permission, prohibition) in legal documents can convey critical information, and identification of deontic modalities is often performed using Natural Language Processing (NLP) techniques as a Deontic Modality Classification' (DMC) text classification task. As deontic modalities in legal text are not mutually exclusive, a key challenge with DMC is that it classifies the provided text into a single modality while in reality it might have multiple deontic modalities. To address this, this study analyzes the feasibility of performing deontic modality identification as a Named Entity Recognition (NER) task over DMC task approaches in a low-resource data setting with EU legislation. Low-resource NLP approaches can offer solutions to tackle the problem of scarce data. In this paper, we use a rule-based approach with modal verbs and a Decision Tree classifier for DMC task. For NER, we utilize Conditional Random Fields (CRFs) in a low-resource setting and report on the reliability and precision for identification of deontic modality. Our experiments reveal that simpler models, like decision trees, out perform larger models in the low-resource setting of DMC obtaining macro-F1 score of 0.83. For the NER task, the CRF models show consistent performance forobligation' labels with an F1-score of 0.51 but have wavering results for other classes with a max F1-score of 0.26 for permission', and 0.08 forprohibition'.

Lawnotation, 1.0.0