Abstract
A key component of the Natural Language Processing (NLP) pipeline is Sentence Boundary Detection (SBD). Erroneous SBD could affect other processing steps and reduce performance. A few criteria based on punctuation and capitalization are necessary to identify sentence borders in well-defined corpora. However, due to several grammatical ambiguities, the complex structure of legal data poses difficulties for SBD. In this paper, we have trained a neural network framework for identifying the end of the sentence in legal text. We used several state-of-the-art deep learning models, analyzed their performance, and identified that Convolutional Neural Network(CNN) outperformed other deep learning frameworks. We compared the results with rule-based, statistical, and transformer-based frameworks. The best neural network model outscored the popular rule-based framework with an improvement of 8% in the F1 score. Although domain-specific statistical models have slightly improved performance, the trained CNN is 80 times faster in run-time and doesn’t require much feature engineering. Furthermore, after extensive pretraining, the transformer models fall short in overall performance compared to the best deep learning model.- Anthology ID:
- 2022.nllp-1.18
- Volume:
- Proceedings of the Natural Legal Language Processing Workshop 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Venue:
- NLLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 208–217
- Language:
- URL:
- https://aclanthology.org/2022.nllp-1.18
- DOI:
- Cite (ACL):
- Reshma Sheik, Gokul T, and S Nirmala. 2022. Efficient Deep Learning-based Sentence Boundary Detection in Legal Text. In Proceedings of the Natural Legal Language Processing Workshop 2022, pages 208–217, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Efficient Deep Learning-based Sentence Boundary Detection in Legal Text (Sheik et al., NLLP 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.nllp-1.18.pdf