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%).

本文介绍了一项浅层分析任务，并针对此任务生成少量领域特定数据集。作者使用此数据集训练了一个序列标注器，其测试结果达到了 79.93 的 F1 分数并可以识别建筑规范文档中的大多数定义术语和 Multi-Word Expressions。