SPaR.txt, a cheap Shallow Parsing approach for Regulatory texts
This work addresses the problem of parsing regulatory texts for automated compliance checking, but it is incremental as it focuses on a small, domain-specific task.
The study tackled the challenge of automated compliance checking by introducing a shallow parsing task with cheap training data, achieving a 79.93 F1-score on a test set and identifying 89.84% of defined terms in building regulations.
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%).