Requirement Text Detection from Contract Packages to Support Project Definition Determination
dc.contributor.author | Le, Tuyen | |
dc.contributor.author | Le, Chau | |
dc.contributor.author | Jeong, H. David | |
dc.contributor.author | Chukharev-Hudilainen, Evgeny | |
dc.contributor.author | Gilbert, Stephen | |
dc.contributor.department | Virtual Reality Applications Center | |
dc.contributor.department | Psychology | |
dc.contributor.department | Department of English | |
dc.contributor.department | Department of Industrial and Manufacturing Systems Engineering | |
dc.contributor.department | Gerontology | |
dc.contributor.department | Human Computer Interaction | |
dc.date | 2021-01-21T18:31:55.000 | |
dc.date.accessioned | 2021-02-25T18:44:03Z | |
dc.date.available | 2021-02-25T18:44:03Z | |
dc.date.copyright | Tue Jan 01 00:00:00 UTC 2019 | |
dc.date.embargo | 2021-01-21 | |
dc.date.issued | 2019-01-01 | |
dc.description.abstract | <p>Project requirements are wishes and expectations of the client toward the design, construction, and other project management processes. The project definition is typically specified in a contract package including a contract document and many other related documents such as drawings, specifications, and government codes. Project definition determination is critical to the success of a project. Due to the lack of efficient tools for requirement processing, the current practices regarding project scoping still heavily rely on a manual basis which is tedious, time-consuming, and error-prone. This study aims to fill that gap by developing an automated method for identifying requirement texts from contractual documents. The study employed Naïve Bayes to train a classification model that can be used to separate requirement statements from non-requirement statements. An experiment was conducted on a manually labeled dataset of 1191 statements. The results revealed that the developed requirement detection model achieves a promising accuracy of over 90%.</p> | |
dc.description.comments | <p>This is a post-peer-review, pre-copyedit version of a proceeding published as Le, Tuyen, Chau Le, H. David Jeong, Stephen B. Gilbert, and Evgeny Chukharev-Hudilainen. "Requirement text detection from contract packages to support project definition determination." In: <em>Advances in Informatics and Computing in Civil and Construction Engineering</em> (2019): 569-576. The final authenticated version is available online at DOI: <a href="https://doi.org/10.1007/978-3-030-00220-6_68" target="_blank">10.1007/978-3-030-00220-6_68</a>. Posted with permission.</p> | |
dc.format.mimetype | application/pdf | |
dc.identifier | archive/lib.dr.iastate.edu/engl_conf/13/ | |
dc.identifier.articleid | 1010 | |
dc.identifier.contextkey | 21208702 | |
dc.identifier.s3bucket | isulib-bepress-aws-west | |
dc.identifier.submissionpath | engl_conf/13 | |
dc.identifier.uri | https://dr.lib.iastate.edu/handle/20.500.12876/94209 | |
dc.language.iso | en | |
dc.source.bitstream | archive/lib.dr.iastate.edu/engl_conf/13/2019_ChukharevHudilainen_RequirementText.pdf|||Fri Jan 14 19:41:31 UTC 2022 | |
dc.source.uri | 10.1007/978-3-030-00220-6_68 | |
dc.subject.disciplines | Applied Linguistics | |
dc.subject.disciplines | Construction Engineering and Management | |
dc.subject.disciplines | Ergonomics | |
dc.subject.disciplines | Operational Research | |
dc.subject.keywords | Project definition | |
dc.subject.keywords | Requirement management | |
dc.subject.keywords | Requirement extraction | |
dc.subject.keywords | Machine learning | |
dc.subject.keywords | Natural language processing | |
dc.subject.keywords | Text classification | |
dc.subject.keywords | Naïve bayes | |
dc.title | Requirement Text Detection from Contract Packages to Support Project Definition Determination | |
dc.type | article | |
dc.type.genre | conference | |
dspace.entity.type | Publication | |
relation.isAuthorOfPublication | 8bf3ebd3-e598-47a4-9959-d9e716eb081d | |
relation.isAuthorOfPublication | a3a8c6a1-90cd-4fa0-9cf3-316a1535958d | |
relation.isOrgUnitOfPublication | dad3cd36-0f8b-49c3-b43f-1df139ae2068 | |
relation.isOrgUnitOfPublication | 796236b3-85a0-4cde-b154-31da9e94ed42 | |
relation.isOrgUnitOfPublication | a7f2ac65-89b1-4c12-b0c2-b9bb01dd641b | |
relation.isOrgUnitOfPublication | 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1 |
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