SP a R .txt, a Cheap Shallow Parsing Approach for Regulatory Texts (2021)

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%).

## introduction non-compliance with building regulations has been linked to fatal incidents @xcite . however, ensuring that a building complies with regulations is complicated and time-consuming because: automated compliance checking (acc) could reduce the difficulty, time, costs and number of human errors made during compliance checking @xcite , as well as ease customisation and innovation in the building sector @xcite . there exist two branches of acc research. one focuses on tools that reason over a rule-base -often consisting of hard-coded rules @xcite . the other branch attempts semantically parse the natural language regulations into rules that enable reasoning -a complex task that is of interest to the wider legal domain @xcite . this study presents a novel shallow parsing task, for which the creation of training data is cheap, and an accompanying small dataset of 200 sentences. the aim is to learn a semantic lexicon for acc, which is often an important first step for semantic parsing because it enables the grounding of information units identified in a text -such as objects, interactions and constraints @xcite @xcite . section 2 motivates our task and describes related work with a focus on parsing building regulations. 3 describes the task, as well as the collection of a small annotated dataset -spar.txt -for the task of discovering and identifying domain-specific terms, including multi-word expressionss (mwe). in 4 we describe and train a sequence tagging model, which generalises well to unseen text within the same domain. 5 describes the evaluation of outputs, specifically with regards to the objects identified in a corpus of 13k sentences derived from the scot-tish building regulations (scottish @xcite . our model finds 89,84% of the terms explicitly defined in these documents. furthermore, a significant proportion (70,3%) of these predictions matches exactly what a human annotator would consider to be an object in a given sentence context. we argue that our new task can provide a cheap approach to lexicon learning that could benefit (1) information extraction (ie) in support of acc, (2) information retrieval (ir) in support of manual compliance checking, and (3) the mapping of unstructured text to a structured representation. in the acc domain, studies on semantic parsing rely on traditional rule-based systems to extract concepts and construct logical statements, e.g., @xcite @xcite . empirical approaches may be able to handle the combinatorial explosion caused by ambiguity in natural language @xcite . but training data for such systems is difficult and costly to collect @xcite . unsurprisingly, machine learning studies in the acc domain focus on simpler sub-tasks of semantic parsing for which training data is relatively easy to collect, such as named entity recognition @xcite and relation classification @xcite . this study follows a similar strategy to limit the complexity of annotation, see 3.2 and 3.3. ## evaluation evaluating discovered lexical entries can be tricky @xcite . we limit our evaluation to the processing of object spans. first, we perform an extrinsic evaluation by comparing our model output against the defined terms in the scotreg ## conclusions regulatory documents are an important part of the legal framework, with research on acc methods focusing on the grand goal of semantic parsing. this study introduces a much simpler parsing task that requires few training examples, with the additional benefit that the collection of a dataset is cheap. we presented the small spar.txt dataset and trained a sequence tagger that can process single-word and multi-word spans. we showed that the object spans identified in the scotreg corpus cover most of the existing, limited set of defined terms. moreover, the model achieves reasonable accuracy when it comes to discovering object spans, regardless of whether these are discontiguous or not. the annotation of gold training data for the presented approach is cheap, because the annotation task is simple. but the results can benefit the research on acc, e.g., the output of our task may support more complicated semantic annotation tasks, ie and ir, as well as the development of a domainspecific lexicon. future work will focus on clus-tering the identified spans to develop a semantic lexicon, balancing and growing the dataset, as well as using predicted outputs for ir in support of manual acc. finally, we will explore how well the presented approach performs in other domains with similar text characteristics. a overview of spans and tags for spar.txt and scotreg predictions rooms intended for sleeping should be separated by a door that will act as a sound barrier and reduce noise transference.