Semantic NLP Based Information Extraction from Construction Regulatory Documents for Automated Compliance Checking
AbstractAutomated regulatory compliance checking requires automated extraction of requirements from regulatory textual documents and their formalization in a computer processable rule representation. Such information extraction (IE) is a challenging task that requires complex analysis and processing of text. Natural language processing (NLP) aims to enable computers to process natural language text in a human like manner. This paper proposes a semantic, rule based NLP approach for automated IE from construction regulatory documents. The proposed approach uses a set of pattern matching based IE rules and conflict resolution (CR) rules in IE. A variety of syntactic (syntax/grammar related) and semantic (meaning/context related) text features are used in the patterns of the IE and CR rules. Phrase structure grammar (PSG) based phrasal tags and separation and sequencing of semantic information elements are proposed and used to reduce the number of needed patterns. An ontology is used to aid in the recognition.