{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/30015"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/30015","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"Identifying External Cross-references using Natural Language Processing (NLP)","abstract":"[Context and motivation] Software engineers build systems that need to be compliant with relevant regulations. These regulations are stated in authoritative documents from which regulatory requirements need to be elicited. Project contract contains cross-references to these regulatory requirements in external documents. [Problem] Exploring and identifying the regulatory requirements in voluminous textual data is enormously time consuming, and hence costly, and error-prone in sizable software projects. [Principal idea and novelty] We use Natural Language Processing (NLP), Pattern Recognition and Web Scrapping techniques for automatically extracting external cross-references from contractual requirements and prepare a map for representing related external cross-references to each contractual requirement. This map is also automatically extended to the world-wide web using previously identified references that are not located in local resources. The novel aspects in our approach involve: (i) a taxonomy of semantic cues for identifying cross-references, (ii) a taxonomy of grammatical structures for supporting various combinations of word roles in a sentence, (iii) APA standards for validating cross-references, and (iv) third party access for unavailable resources. [Research Contribution] The key research contribution is a tool implementing the mentioned techniques for identifying cross-references in contractual documents and related regulatory documents and the web. The tool produces high-level and detailed views of cross-references amongst documents that can be used by various stakeholders for project management, requirements elicitation, testing, and other purposes. We anticipate that this would save an enormous amount of time and effort needed to do this task manually in contractual projects. [Conclusion] The output cross-references produced by the tool suggests a precision of 99%, and recall of 87% from contractual requirements. Further work is identified.","abstract_html":"[Context and motivation] Software engineers build systems that need to be compliant with relevant regulations. These regulations are stated in authoritative documents from which regulatory requirements need to be elicited. Project contract contains cross-references to these regulatory requirements in external documents. [Problem] Exploring and identifying the regulatory requirements in voluminous textual data is enormously time consuming, and hence costly, and error-prone in sizable software projects. [Principal idea and novelty] We use Natural Language Processing (NLP), Pattern Recognition and Web Scrapping techniques for automatically extracting external cross-references from contractual requirements and prepare a map for representing related external cross-references to each contractual requirement. This map is also automatically extended to the world-wide web using previously identified references that are not located in local resources. The novel aspects in our approach involve: (i) a taxonomy of semantic cues for identifying cross-references, (ii) a taxonomy of grammatical structures for supporting various combinations of word roles in a sentence, (iii) APA standards for validating cross-references, and (iv) third party access for unavailable resources. [Research Contribution] The key research contribution is a tool implementing the mentioned techniques for identifying cross-references in contractual documents and related regulatory documents and the web. The tool produces high-level and detailed views of cross-references amongst documents that can be used by various stakeholders for project management, requirements elicitation, testing, and other purposes. We anticipate that this would save an enormous amount of time and effort needed to do this task manually in contractual projects. [Conclusion] The output cross-references produced by the tool suggests a precision of 99%, and recall of 87% from contractual requirements. Further work is identified.","abstract_has_math":false,"creators":["Rahmani, Elham"],"institution":"The University of Western Ontario","degree_name":"M Sc","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Nazim H. Madhavji"],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-04-23","date_published":"2020-04-23","updated_at":"2026-07-27T21:55:56Z","subjects":["Elham Rahmani","Regulatory Compliance","Regulatory Requirements","Cross-reference","Natural Language Processing","Pattern Recognition"],"languages":["en_ca"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14721/30015","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Nazim H. Madhavji"]},{"key":"dc:creator","label":"Author","values":["Rahmani, Elham"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-10T18:36:18Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-10T18:36:18Z"]},{"key":"dc:date.issued","label":"Date","values":["2020-04-23"]},{"key":"dc:publisher","label":"Institution","values":["The University of Western Ontario"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M Sc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Elham Rahmani","Regulatory Compliance","Regulatory Requirements","Cross-reference","Natural Language Processing","Pattern Recognition"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_ca"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/20.500.14721/30015"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Collaborative Specialization: Artificial Intelligence","The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."]},{"key":"dc:description.abstract","label":"Abstract","values":["[Context and motivation] Software engineers build systems that need to be compliant with relevant regulations. These regulations are stated in authoritative documents from which regulatory requirements need to be elicited. Project contract contains cross-references to these regulatory requirements in external documents. [Problem] Exploring and identifying the regulatory requirements in voluminous textual data is enormously time consuming, and hence costly, and error-prone in sizable software projects. [Principal idea and novelty] We use Natural Language Processing (NLP), Pattern Recognition and Web Scrapping techniques for automatically extracting external cross-references from contractual requirements and prepare a map for representing related external cross-references to each contractual requirement. This map is also automatically extended to the world-wide web using previously identified references that are not located in local resources. The novel aspects in our approach involve: (i) a taxonomy of semantic cues for identifying cross-references, (ii) a taxonomy of grammatical structures for supporting various combinations of word roles in a sentence, (iii) APA standards for validating cross-references, and (iv) third party access for unavailable resources. [Research Contribution] The key research contribution is a tool implementing the mentioned techniques for identifying cross-references in contractual documents and related regulatory documents and the web. The tool produces high-level and detailed views of cross-references amongst documents that can be used by various stakeholders for project management, requirements elicitation, testing, and other purposes. We anticipate that this would save an enormous amount of time and effort needed to do this task manually in contractual projects. [Conclusion] The output cross-references produced by the tool suggests a precision of 99%, and recall of 87% from contractual requirements. Further work is identified."]},{"key":"dc:title","label":"Title","values":["Identifying External Cross-references using Natural Language Processing (NLP)"]}]}],"canonical_facts":{"dc:contributor.advisor":["Nazim H. Madhavji"],"dc:creator":["Rahmani, Elham"],"dc:date.accessioned":["2025-07-10T18:36:18Z"],"dc:date.available":["2025-07-10T18:36:18Z"],"dc:date.issued":["2020-04-23"],"dc:description":["Collaborative Specialization: Artificial Intelligence","The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."],"dc:description.abstract":["[Context and motivation] Software engineers build systems that need to be compliant with relevant regulations. These regulations are stated in authoritative documents from which regulatory requirements need to be elicited. Project contract contains cross-references to these regulatory requirements in external documents. [Problem] Exploring and identifying the regulatory requirements in voluminous textual data is enormously time consuming, and hence costly, and error-prone in sizable software projects. [Principal idea and novelty] We use Natural Language Processing (NLP), Pattern Recognition and Web Scrapping techniques for automatically extracting external cross-references from contractual requirements and prepare a map for representing related external cross-references to each contractual requirement. This map is also automatically extended to the world-wide web using previously identified references that are not located in local resources. The novel aspects in our approach involve: (i) a taxonomy of semantic cues for identifying cross-references, (ii) a taxonomy of grammatical structures for supporting various combinations of word roles in a sentence, (iii) APA standards for validating cross-references, and (iv) third party access for unavailable resources. [Research Contribution] The key research contribution is a tool implementing the mentioned techniques for identifying cross-references in contractual documents and related regulatory documents and the web. The tool produces high-level and detailed views of cross-references amongst documents that can be used by various stakeholders for project management, requirements elicitation, testing, and other purposes. We anticipate that this would save an enormous amount of time and effort needed to do this task manually in contractual projects. [Conclusion] The output cross-references produced by the tool suggests a precision of 99%, and recall of 87% from contractual requirements. Further work is identified."],"dc:identifier.uri":["https://hdl.handle.net/20.500.14721/30015"],"dc:language.iso":["en_ca"],"dc:publisher":["The University of Western Ontario"],"dc:subject":["Elham Rahmani","Regulatory Compliance","Regulatory Requirements","Cross-reference","Natural Language Processing","Pattern Recognition"],"dc:title":["Identifying External Cross-references using Natural Language Processing (NLP)"],"dc:type":["thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["M Sc"]},"updated_at":"2026-07-27T21:55:56Z"}