Machine Learning and Conceptual Reasoning for Inconsistency Detection
This paper focuses on detecting inconsistencies within text corpora. It is a very interesting area with many applications. Most existing methods deal with this problem using complicated textual analysis, which is known for not being accurate enough. We propose a new methodology that consists of two...
| Published in: | IEEE Access |
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| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2017-01-01
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| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7792212/ |
| _version_ | 1852762918343409664 |
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| author | Jameela Al Otaibi Zeineb Safi Abdelaali Hassaine Fahad Islam Ali Jaoua |
| author_facet | Jameela Al Otaibi Zeineb Safi Abdelaali Hassaine Fahad Islam Ali Jaoua |
| author_sort | Jameela Al Otaibi |
| collection | DOAJ |
| container_title | IEEE Access |
| description | This paper focuses on detecting inconsistencies within text corpora. It is a very interesting area with many applications. Most existing methods deal with this problem using complicated textual analysis, which is known for not being accurate enough. We propose a new methodology that consists of two steps, the first one being a machine learning step that performs multilevel text categorization. The second one applies conceptual reasoning on the predicted categories in order to detect inconsistencies. This paper has been validated on a set of Islamic advisory opinions (also known as fatwas). This domain is gaining a large interest with users continuously checking the authenticity and relevance of such content. The results show that our method is very accurate and can complement existing methods using the linguistic analysis. |
| format | Article |
| id | doaj-art-96d397fa5d1246a2aabc856dd9757d5e |
| institution | Directory of Open Access Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| spelling | doaj-art-96d397fa5d1246a2aabc856dd9757d5e2025-08-19T20:55:12ZengIEEEIEEE Access2169-35362017-01-01533834610.1109/ACCESS.2016.26424027792212Machine Learning and Conceptual Reasoning for Inconsistency DetectionJameela Al Otaibi0Zeineb Safi1https://orcid.org/0000-0003-0526-8949Abdelaali Hassaine2Fahad Islam3Ali Jaoua4https://orcid.org/0000-0001-6578-8191Computer Science and Engineering Department, College of Engineering, Qatar University, Doha, QatarComputer Science and Engineering Department, College of Engineering, Qatar University, Doha, QatarComputer Science and Engineering Department, College of Engineering, Qatar University, Doha, QatarComputer Science and Engineering Department, College of Engineering, Qatar University, Doha, QatarComputer Science and Engineering Department, College of Engineering, Qatar University, Doha, QatarThis paper focuses on detecting inconsistencies within text corpora. It is a very interesting area with many applications. Most existing methods deal with this problem using complicated textual analysis, which is known for not being accurate enough. We propose a new methodology that consists of two steps, the first one being a machine learning step that performs multilevel text categorization. The second one applies conceptual reasoning on the predicted categories in order to detect inconsistencies. This paper has been validated on a set of Islamic advisory opinions (also known as fatwas). This domain is gaining a large interest with users continuously checking the authenticity and relevance of such content. The results show that our method is very accurate and can complement existing methods using the linguistic analysis.https://ieeexplore.ieee.org/document/7792212/Information extractionconceptual reasoningtext categorizationhyper rectangular decompositioninconsistency detection |
| spellingShingle | Jameela Al Otaibi Zeineb Safi Abdelaali Hassaine Fahad Islam Ali Jaoua Machine Learning and Conceptual Reasoning for Inconsistency Detection Information extraction conceptual reasoning text categorization hyper rectangular decomposition inconsistency detection |
| title | Machine Learning and Conceptual Reasoning for Inconsistency Detection |
| title_full | Machine Learning and Conceptual Reasoning for Inconsistency Detection |
| title_fullStr | Machine Learning and Conceptual Reasoning for Inconsistency Detection |
| title_full_unstemmed | Machine Learning and Conceptual Reasoning for Inconsistency Detection |
| title_short | Machine Learning and Conceptual Reasoning for Inconsistency Detection |
| title_sort | machine learning and conceptual reasoning for inconsistency detection |
| topic | Information extraction conceptual reasoning text categorization hyper rectangular decomposition inconsistency detection |
| url | https://ieeexplore.ieee.org/document/7792212/ |
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