SPaR.txt, a Cheap Shallow Parsing Approach for Regulatory Texts

Ruben Kruiper, Ioannis Konstas, Alasdair J.G. Gray, Farhad Sadeghineko, Richard Watson, Bimal Kumar


Abstract
Automated Compliance Checking (ACC) systems aim to semantically parse building regulations to a set of rules. However, semantic parsing is known to be hard and requires large amounts of training data. The complexity of creating such training data has led to research that focuses on small sub-tasks, such as shallow parsing or the extraction of a limited subset of rules. This study introduces a shallow parsing task for which training data is relatively cheap to create, with the aim of learning a lexicon for ACC. We annotate a small domain-specific dataset of 200 sentences, SPaR.txt, and train a sequence tagger that achieves 79,93 F1-score on the test set. We then show through manual evaluation that the model identifies most (89,84%) defined terms in a set of building regulation documents, and that both contiguous and discontiguous Multi-Word Expressions (MWE) are discovered with reasonable accuracy (70,3%).
Anthology ID:
2021.nllp-1.14
Volume:
Proceedings of the Natural Legal Language Processing Workshop 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | NLLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
129–143
Language:
URL:
https://aclanthology.org/2021.nllp-1.14
DOI:
10.18653/v1/2021.nllp-1.14
Bibkey:
Cite (ACL):
Ruben Kruiper, Ioannis Konstas, Alasdair J.G. Gray, Farhad Sadeghineko, Richard Watson, and Bimal Kumar. 2021. SPaR.txt, a Cheap Shallow Parsing Approach for Regulatory Texts. In Proceedings of the Natural Legal Language Processing Workshop 2021, pages 129–143, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
SPaR.txt, a Cheap Shallow Parsing Approach for Regulatory Texts (Kruiper et al., NLLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.nllp-1.14.pdf
Code
 rubenkruiper/spar.txt