Journal
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
Volume 30, Issue 1, Pages -Publisher
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)CP.1943-5487.0000301
Keywords
Automated compliance checking; Semantic systems; Automated construction management systems; Natural language processing; Text classification; Machine learning
Funding
- National Science Foundation [1201170]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1201170] Funding Source: National Science Foundation
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Automated regulatory and contractual compliance checking requires automated rule extraction from regulatory and contractual textual documents (e.g., contract specifications). Automated rule extraction is a challenging task that requires complex processing of text. In the proposed automated compliance checking (ACC) approach, the first step in automating the rule extraction process is automatically classifying the different documents and parts of documents (e.g., contract clauses) into predefined categories (environmental, safety, health, etc.) for preparing it for further text analysis and rule extraction. These categories are defined in a semantic model for normative reasoning. This paper presents a semantic, machine learning-based text classification algorithm for classifying clauses and subclauses of general conditions for supporting ACC in construction. The multilabel classification problem was transformed into a set of binary classification problems. Different machine learning algorithms, text preprocessing techniques, methods of text feature scoring, methods of feature weighting, and feature sizes were implemented and evaluated at different thresholds. The developed classifier achieved 100 and 96% recall and precision, respectively, on the testing data. (C) 2014 American Society of Civil Engineers.
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