Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Azzi, S.; Daramola, O.; Gagnon, S.
Health information technology acceptance by older people: a systematic review Article de journal
Dans: Discover public health, vol. 23, no 1, 2026, ISSN: 30050774 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Acceptance, Adoption, adult, Africa, aged, aging, assistive technology, conceptual framework, Elderly people, female, Healthcare technology, human, Information retrieval, male, Medical informatics, older adults, Preferred Reporting Items for Systematic Reviews and Meta-Analyses, quality control, review, Social Cognitive Theory, systematic review, telehealth, Theory of Planned Behavior, Theory of Reasoned Action, validity, Web of Science
@article{azziHealthInformationTechnology2026,
title = {Health information technology acceptance by older people: a systematic review},
author = {S. Azzi and O. Daramola and S. Gagnon},
url = {https://www.scopus.com/pages/publications/105035521505?origin=resultslist},
doi = {10.1186/s12982-026-01658-y},
issn = {30050774 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Discover public health},
volume = {23},
number = {1},
publisher = {BioMed Central Ltd},
abstract = {Background: The increasing ageing population has led to the development of Health Information Technologies (HIT) for older individuals. However, there is a lack of clarity on which HIT older people are more likely to accept and the factors influencing their intention for acceptance. Methods: A systematic literature review was conducted following PRISMA guidelines. Four academic databases were searched: Scopus, PubMed, the Web of Science, and the Cumulative Index to Nursing and Allied Health Literature, which resulted in the retrieval of 4941 articles. A total of 51 primary studies from 2007 to 2025 that satisfied the inclusion criteria and were subjected to quality assessment and used for the study. This review was registered in PROSPERO (CRD 420251271463). Results: The study revealed that older people have a higher intention to accept assistive technologies over mobile health/eHealth. Older people’s HIT acceptance is influenced by personal, social, and technology design factors. There is a lack of geographical diversity of studies, with few coming from regions like Asia and Africa, hindering a global understanding of HIT acceptance factors. Notably, nearly half of the studies (45.09%) did not employ a theoretical framework, suggesting a lack of grounding in established knowledge and theoretical validity. While theories like the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology and their extensions are commonly used, other frameworks, such as the Theory of Reasoned Action, Theory of Planned Behaviour, and Social Cognitive Theory, have been underexplored. Conclusion: The study highlights the need for more rigorous research with diverse geographical representation and the application of broader theoretical perspectives to deepen understanding of HIT acceptance among older people. It also specifies the imperatives for user-centredness in developing HIT products by manufacturers. © The Author(s) 2026.},
keywords = {Acceptance, Adoption, adult, Africa, aged, aging, assistive technology, conceptual framework, Elderly people, female, Healthcare technology, human, Information retrieval, male, Medical informatics, older adults, Preferred Reporting Items for Systematic Reviews and Meta-Analyses, quality control, review, Social Cognitive Theory, systematic review, telehealth, Theory of Planned Behavior, Theory of Reasoned Action, validity, Web of Science},
pubstate = {published},
tppubtype = {article}
}
Background: The increasing ageing population has led to the development of Health Information Technologies (HIT) for older individuals. However, there is a lack of clarity on which HIT older people are more likely to accept and the factors influencing their intention for acceptance. Methods: A systematic literature review was conducted following PRISMA guidelines. Four academic databases were searched: Scopus, PubMed, the Web of Science, and the Cumulative Index to Nursing and Allied Health Literature, which resulted in the retrieval of 4941 articles. A total of 51 primary studies from 2007 to 2025 that satisfied the inclusion criteria and were subjected to quality assessment and used for the study. This review was registered in PROSPERO (CRD 420251271463). Results: The study revealed that older people have a higher intention to accept assistive technologies over mobile health/eHealth. Older people’s HIT acceptance is influenced by personal, social, and technology design factors. There is a lack of geographical diversity of studies, with few coming from regions like Asia and Africa, hindering a global understanding of HIT acceptance factors. Notably, nearly half of the studies (45.09%) did not employ a theoretical framework, suggesting a lack of grounding in established knowledge and theoretical validity. While theories like the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology and their extensions are commonly used, other frameworks, such as the Theory of Reasoned Action, Theory of Planned Behaviour, and Social Cognitive Theory, have been underexplored. Conclusion: The study highlights the need for more rigorous research with diverse geographical representation and the application of broader theoretical perspectives to deepen understanding of HIT acceptance among older people. It also specifies the imperatives for user-centredness in developing HIT products by manufacturers. © The Author(s) 2026.
2.
Boudra, N.; Elhajjout, A.; Moudoud, H.; Oujaoura, M.; Jarir, Z.; Houda, Z. A. El
Toward Lightweight IoC Extraction in IoT: The Role of Small Language Models Article d'actes
Dans: Int. Congr. Smart Agric. Sustain. Syst., SmartAgri SuSY, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833157801-5 (ISBN), (Journal Abbreviation: Int. Congr. Smart Agric. Sustain. Syst., SmartAgri SuSY).
Résumé | Liens | BibTeX | Étiquettes: Artificial intelligence, Cyber threats, Data-source, Edge Computing, extraction, Indicator of compromize, Indicators of Compromise, Information retrieval, Information Security, IoT Security, Language model, Malware, Natural languages, Network security, Program processors, Resource Constraint, Resource Constraints, Semantics, Small language model, Small Language Models, Threat Intelligence
@inproceedings{boudraLightweightIoCExtraction2025,
title = {Toward Lightweight IoC Extraction in IoT: The Role of Small Language Models},
author = {N. Boudra and A. Elhajjout and H. Moudoud and M. Oujaoura and Z. Jarir and Z. A. El Houda},
url = {https://www.scopus.com/pages/publications/105037622873?origin=resultslist},
doi = {10.1109/SmartAgriSuSY68475.2025.11466843},
isbn = {979-833157801-5 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Int. Congr. Smart Agric. Sustain. Syst., SmartAgri SuSY},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The proliferation of cyber threats necessitates rapid and accurate extraction of Indicators of Compromise (IoCs) from diverse security data sources. While Large Language Models (LLMs) have demonstrated exceptional capabilities in natural language processing and information extraction tasks, their deployment on resource-constrained IoT devices remains challenging due to computational and memory requirements. This paper investigates whether Small Language Models (SLMs) can serve as effective alternatives to LLMs for IoC extraction in IoT environments with limited resources. We present a novel direct LLM-based IoC extraction system leveraging context memory mechanisms for document-wide semantic understanding, specifically designed for deployment on edge computing infrastructure with consumer-grade hardware. Our experimental setup utilizes an RTX 4080 GPU and Ryzen 7 7700X processor running the Ollama framework with GPT-OSS:20B model to evaluate the feasibility of using smaller models instead of resource-intensive LLMs for security tasks. Evaluated on 9 diverse threat intelligence reports spanning different malware families and attack campaigns, the system achieved an average F1 score of 0.62, precision of 0.54, recall of 0.79, and accuracy of 0.85, demonstrating that SLMs can achieve acceptable accuracy levels for IoC extraction while operating within the computational constraints typical of IoT edge deployments. The results suggest that smaller models may provide viable alternatives to large language models for distributed threat intelligence processing in resource-limited environments. © 2025 IEEE.},
note = {Journal Abbreviation: Int. Congr. Smart Agric. Sustain. Syst., SmartAgri SuSY},
keywords = {Artificial intelligence, Cyber threats, Data-source, Edge Computing, extraction, Indicator of compromize, Indicators of Compromise, Information retrieval, Information Security, IoT Security, Language model, Malware, Natural languages, Network security, Program processors, Resource Constraint, Resource Constraints, Semantics, Small language model, Small Language Models, Threat Intelligence},
pubstate = {published},
tppubtype = {inproceedings}
}
The proliferation of cyber threats necessitates rapid and accurate extraction of Indicators of Compromise (IoCs) from diverse security data sources. While Large Language Models (LLMs) have demonstrated exceptional capabilities in natural language processing and information extraction tasks, their deployment on resource-constrained IoT devices remains challenging due to computational and memory requirements. This paper investigates whether Small Language Models (SLMs) can serve as effective alternatives to LLMs for IoC extraction in IoT environments with limited resources. We present a novel direct LLM-based IoC extraction system leveraging context memory mechanisms for document-wide semantic understanding, specifically designed for deployment on edge computing infrastructure with consumer-grade hardware. Our experimental setup utilizes an RTX 4080 GPU and Ryzen 7 7700X processor running the Ollama framework with GPT-OSS:20B model to evaluate the feasibility of using smaller models instead of resource-intensive LLMs for security tasks. Evaluated on 9 diverse threat intelligence reports spanning different malware families and attack campaigns, the system achieved an average F1 score of 0.62, precision of 0.54, recall of 0.79, and accuracy of 0.85, demonstrating that SLMs can achieve acceptable accuracy levels for IoC extraction while operating within the computational constraints typical of IoT edge deployments. The results suggest that smaller models may provide viable alternatives to large language models for distributed threat intelligence processing in resource-limited environments. © 2025 IEEE.
3.
Amamou, H.; Gagnon, S.; Davoust, A.; Avila, A. R.
Towards Robust Retrieval-Augmented Generation Based on Knowledge Graph: A Comparative Analysis Article d'actes
Dans: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern., p. 80–85, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 1062922X (ISSN); 979-833153358-8 (ISBN), (Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.).
Résumé | Liens | BibTeX | Étiquettes: Benchmarking, Comparative analyzes, External sources, Generation systems, Information integration, Information retrieval, Knowledge graph, Knowledge graphs, Language model, Model response, Noise robustness, Pre-training, Prior-knowledge
@inproceedings{amamouRobustRetrievalAugmentedGeneration2025,
title = {Towards Robust Retrieval-Augmented Generation Based on Knowledge Graph: A Comparative Analysis},
author = {H. Amamou and S. Gagnon and A. Davoust and A. R. Avila},
url = {https://www.scopus.com/pages/publications/105033145604?origin=resultslist},
doi = {10.1109/SMC58881.2025.11343466},
isbn = {1062922X (ISSN); 979-833153358-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
pages = {80–85},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Retrieval-Augmented Generation (RAG) was first introduced to enhance the capabilities of Large Language Models (LLMs) beyond their encoded-prior knowledge. This is achieved by providing LLMs with an external source of knowledge, which helps to reduce factual hallucinations and enables the access to new information, typically not available during their pretraining phase. Despite its benefits, there is an increasing concern with the impact of inconsistent retrieved information towards LLMs' responses. Hence, the Retrieval-Augmented Generation Benchmark (RGB) was introduced as a new testbed for RAG evaluation, meant to assess the robustness of LLMs towards inconsistency in the retrieved information. In this work, we use the RGB corpus to evaluate LLMs in four scenarios: (1) noise robustness; (2) information integration; (3) negative rejection; and (4) counterfactual robustness. We perform a comparative analysis between the RAG baseline defined by the RGB and variations of GraphRAG, which is a RAG system based on a Knowledge Graph (KG) and developed to retrieve relevant information from large documents. We tested GraphRAG under three customization to improve its robustness. Our approach demonstrates improvements compared to the RGB baseline, providing insights on how to design more reliable RAG systems, tailored for real-world scenarios. © 2025 IEEE.},
note = {Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
keywords = {Benchmarking, Comparative analyzes, External sources, Generation systems, Information integration, Information retrieval, Knowledge graph, Knowledge graphs, Language model, Model response, Noise robustness, Pre-training, Prior-knowledge},
pubstate = {published},
tppubtype = {inproceedings}
}
Retrieval-Augmented Generation (RAG) was first introduced to enhance the capabilities of Large Language Models (LLMs) beyond their encoded-prior knowledge. This is achieved by providing LLMs with an external source of knowledge, which helps to reduce factual hallucinations and enables the access to new information, typically not available during their pretraining phase. Despite its benefits, there is an increasing concern with the impact of inconsistent retrieved information towards LLMs' responses. Hence, the Retrieval-Augmented Generation Benchmark (RGB) was introduced as a new testbed for RAG evaluation, meant to assess the robustness of LLMs towards inconsistency in the retrieved information. In this work, we use the RGB corpus to evaluate LLMs in four scenarios: (1) noise robustness; (2) information integration; (3) negative rejection; and (4) counterfactual robustness. We perform a comparative analysis between the RAG baseline defined by the RGB and variations of GraphRAG, which is a RAG system based on a Knowledge Graph (KG) and developed to retrieve relevant information from large documents. We tested GraphRAG under three customization to improve its robustness. Our approach demonstrates improvements compared to the RGB baseline, providing insights on how to design more reliable RAG systems, tailored for real-world scenarios. © 2025 IEEE.
4.
El-Kass, W.; Gagnon, S.; Iglewski, M.
A visual and results-driven rules composition approach for better information extraction Article d'actes
Dans: A., Zaremba M. Sasiadek J. Dolgui (Ed.): IFAC-PapersOnLine, p. 112–117, 2015, ISBN: 24058963 (ISSN), (Journal Abbreviation: IFAC-PapersOnLine).
Résumé | Liens | BibTeX | Étiquettes: Automation, F-score, Flow visualization, Harmonic mean, Information analysis, Information extraction, Information extraction rules, Information retrieval, Rule based, Rule composition, Rules composition, Visual process, Visualization
@inproceedings{el-kass_visual_2015,
title = {A visual and results-driven rules composition approach for better information extraction},
author = {W. El-Kass and S. Gagnon and M. Iglewski},
editor = {Zaremba M. Sasiadek J. Dolgui A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953876235&doi=10.1016%2fj.ifacol.2015.06.067&partnerID=40&md5=3cd38b1d6b9efc819cd882d181cdda92},
doi = {10.1016/j.ifacol.2015.06.067},
isbn = {24058963 (ISSN)},
year = {2015},
date = {2015-01-01},
booktitle = {IFAC-PapersOnLine},
volume = {28},
number = {3},
pages = {112–117},
abstract = {We present a highly visual process for creating and combining elementary information extraction rules, based on their results, in order to find the rules combination that produces the most accurate information extraction results. A rule's accuracy is determined by its F-Score which is the harmonic mean of the precision and the recall of that rule. Rules are combined using logical OR and AND operators. Running a few hundreds rules combinations over a corpus, in order to determine their accuracies, can take days. Using our approach, millions of rules combinations can be tested and their accuracies (F-Score) can be calculated in few seconds. A prototype was created to demonstrate the effectiveness of our approach. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},
note = {Journal Abbreviation: IFAC-PapersOnLine},
keywords = {Automation, F-score, Flow visualization, Harmonic mean, Information analysis, Information extraction, Information extraction rules, Information retrieval, Rule based, Rule composition, Rules composition, Visual process, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a highly visual process for creating and combining elementary information extraction rules, based on their results, in order to find the rules combination that produces the most accurate information extraction results. A rule's accuracy is determined by its F-Score which is the harmonic mean of the precision and the recall of that rule. Rules are combined using logical OR and AND operators. Running a few hundreds rules combinations over a corpus, in order to determine their accuracies, can take days. Using our approach, millions of rules combinations can be tested and their accuracies (F-Score) can be calculated in few seconds. A prototype was created to demonstrate the effectiveness of our approach. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
5.
El-Kass, W.; Gagnon, S.; Iglewski, M.
A visual and results-driven rules composition approach for better information extraction Article d'actes
Dans: A., Zaremba M. Sasiadek J. Dolgui (Ed.): IFAC-PapersOnLine, p. 112–117, 2015, ISBN: 24058963 (ISSN), (Journal Abbreviation: IFAC-PapersOnLine).
Résumé | Liens | BibTeX | Étiquettes: Automation, F-score, Flow visualization, Harmonic mean, Information analysis, Information extraction, Information extraction rules, Information retrieval, Rule based, Rule composition, Rules composition, Visual process, Visualization
@inproceedings{el-kassVisualResultsdrivenRules2015,
title = {A visual and results-driven rules composition approach for better information extraction},
author = {W. El-Kass and S. Gagnon and M. Iglewski},
editor = {Zaremba M. Sasiadek J. Dolgui A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953876235&doi=10.1016%2fj.ifacol.2015.06.067&partnerID=40&md5=3cd38b1d6b9efc819cd882d181cdda92},
doi = {10.1016/j.ifacol.2015.06.067},
isbn = {24058963 (ISSN)},
year = {2015},
date = {2015-01-01},
booktitle = {IFAC-PapersOnLine},
volume = {28},
number = {3},
pages = {112–117},
abstract = {We present a highly visual process for creating and combining elementary information extraction rules, based on their results, in order to find the rules combination that produces the most accurate information extraction results. A rule's accuracy is determined by its F-Score which is the harmonic mean of the precision and the recall of that rule. Rules are combined using logical OR and AND operators. Running a few hundreds rules combinations over a corpus, in order to determine their accuracies, can take days. Using our approach, millions of rules combinations can be tested and their accuracies (F-Score) can be calculated in few seconds. A prototype was created to demonstrate the effectiveness of our approach. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},
note = {Journal Abbreviation: IFAC-PapersOnLine},
keywords = {Automation, F-score, Flow visualization, Harmonic mean, Information analysis, Information extraction, Information extraction rules, Information retrieval, Rule based, Rule composition, Rules composition, Visual process, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a highly visual process for creating and combining elementary information extraction rules, based on their results, in order to find the rules combination that produces the most accurate information extraction results. A rule's accuracy is determined by its F-Score which is the harmonic mean of the precision and the recall of that rule. Rules are combined using logical OR and AND operators. Running a few hundreds rules combinations over a corpus, in order to determine their accuracies, can take days. Using our approach, millions of rules combinations can be tested and their accuracies (F-Score) can be calculated in few seconds. A prototype was created to demonstrate the effectiveness of our approach. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
6.
Gagnon, S.; Messaoudi, S.; Charbonneau, A.
Dans: CORIA 2011: COnference en Recherche d'Information et Applications - Conference on Information Retrieval and Applications, p. 151–158, Avignon, 2011, ISBN: 978-2-35768-024-1.
Résumé | Liens | BibTeX | Étiquettes: Administrative data processing, Automation, Classification (of information), Domain-specific ontologies, Extensible Business Reporting Language (XBRL), F measure, Financial news, Information retrieval, Ontology, Reuters, Text classification, Text processing
@inproceedings{gagnon_automated_2011,
title = {Automated Text Classification based on an ontology standard: Application of the Extensible Business Reporting Language (XBRL) to Reuters Corpus Volume 1 (RCV1) [Classification automatique de textes basée sur une ontologie normée: Application du Extensible Business Reporting Language (XBRL) au Reuters Corpus Volume 1 (RCV1)]},
author = {S. Gagnon and S. Messaoudi and A. Charbonneau},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869423599&partnerID=40&md5=1d12a436b5acb9f715fd6f1669e37be4},
isbn = {978-2-35768-024-1},
year = {2011},
date = {2011-01-01},
booktitle = {CORIA 2011: COnference en Recherche d'Information et Applications - Conference on Information Retrieval and Applications},
pages = {151–158},
address = {Avignon},
abstract = {We demonstrate that applying a domain-specific ontology standard significantly improves Automated Text Classification (ATC). We use the Extensible Business Reporting Language (XBRL) to define a standard ontology and compare the performance of an ACT engine (IBM Classification Module v.8.6) against 2 other list of concepts, namely simple and hierarchical. Our sample of financial news is extracted from the Reuters Corpus Volume 1 (RCV1), where 2 experts in finance help us code 1000 of the 45000 news dealing with mergers and acquisitions. We report recall, precision, the F measure, and in addition a hierarchical measure adjusted for classification relevance in parent classes, as well as a more detailed measure evaluating the classification improvements at the level of each text.},
keywords = {Administrative data processing, Automation, Classification (of information), Domain-specific ontologies, Extensible Business Reporting Language (XBRL), F measure, Financial news, Information retrieval, Ontology, Reuters, Text classification, Text processing},
pubstate = {published},
tppubtype = {inproceedings}
}
We demonstrate that applying a domain-specific ontology standard significantly improves Automated Text Classification (ATC). We use the Extensible Business Reporting Language (XBRL) to define a standard ontology and compare the performance of an ACT engine (IBM Classification Module v.8.6) against 2 other list of concepts, namely simple and hierarchical. Our sample of financial news is extracted from the Reuters Corpus Volume 1 (RCV1), where 2 experts in finance help us code 1000 of the 45000 news dealing with mergers and acquisitions. We report recall, precision, the F measure, and in addition a hierarchical measure adjusted for classification relevance in parent classes, as well as a more detailed measure evaluating the classification improvements at the level of each text.
7.
Gagnon, S.; Messaoudi, S.; Charbonneau, A.
Automated Text Classification based on an ontology standard: Application of the Extensible Business Reporting Language (XBRL) to Reuters Corpus Volume 1 (RCV1) Article de journal
Dans: CORIA 2011: COnference en Recherche d'Information et Applications - Conference on Information Retrieval and Applications, p. 151–158, 2011, ISSN: 978-235768024-1 (ISBN).
Résumé | Liens | BibTeX | Étiquettes: Administrative data processing, Automated Text Classification, Automation, Classification (of information), Domain-specific ontologies, Extensible Business Reporting Language (XBRL), F measure, Financial news, Information retrieval, Ontology, Reuters, Reuters Corpus Volume 1 (RCV1), Text classification, Text processing
@article{gagnonAutomatedTextClassification2011a,
title = {Automated Text Classification based on an ontology standard: Application of the Extensible Business Reporting Language (XBRL) to Reuters Corpus Volume 1 (RCV1)},
author = {S. Gagnon and S. Messaoudi and A. Charbonneau},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869423599&partnerID=40&md5=1d12a436b5acb9f715fd6f1669e37be4},
issn = {978-235768024-1 (ISBN)},
year = {2011},
date = {2011-01-01},
journal = {CORIA 2011: COnference en Recherche d'Information et Applications - Conference on Information Retrieval and Applications},
pages = {151–158},
abstract = {We demonstrate that applying a domain-specific ontology standard significantly improves Automated Text Classification (ATC). We use the Extensible Business Reporting Language (XBRL) to define a standard ontology and compare the performance of an ACT engine (IBM Classification Module v.8.6) against 2 other list of concepts, namely simple and hierarchical. Our sample of financial news is extracted from the Reuters Corpus Volume 1 (RCV1), where 2 experts in finance help us code 1000 of the 45000 news dealing with mergers and acquisitions. We report recall, precision, the F measure, and in addition a hierarchical measure adjusted for classification relevance in parent classes, as well as a more detailed measure evaluating the classification improvements at the level of each text.},
keywords = {Administrative data processing, Automated Text Classification, Automation, Classification (of information), Domain-specific ontologies, Extensible Business Reporting Language (XBRL), F measure, Financial news, Information retrieval, Ontology, Reuters, Reuters Corpus Volume 1 (RCV1), Text classification, Text processing},
pubstate = {published},
tppubtype = {article}
}
We demonstrate that applying a domain-specific ontology standard significantly improves Automated Text Classification (ATC). We use the Extensible Business Reporting Language (XBRL) to define a standard ontology and compare the performance of an ACT engine (IBM Classification Module v.8.6) against 2 other list of concepts, namely simple and hierarchical. Our sample of financial news is extracted from the Reuters Corpus Volume 1 (RCV1), where 2 experts in finance help us code 1000 of the 45000 news dealing with mergers and acquisitions. We report recall, precision, the F measure, and in addition a hierarchical measure adjusted for classification relevance in parent classes, as well as a more detailed measure evaluating the classification improvements at the level of each text.
8.
Gagnon, S.; Messaoudi, S.; Charbonneau, A.
Dans: CORIA 2011: COnference en Recherche d'Information et Applications - Conference on Information Retrieval and Applications, p. 151–158, Avignon, 2011, ISBN: 978-2-35768-024-1.
Résumé | Liens | BibTeX | Étiquettes: Administrative data processing, Automation, Classification (of information), Domain-specific ontologies, Extensible Business Reporting Language (XBRL), F measure, Financial news, Information retrieval, Ontology, Reuters, Text classification, Text processing
@inproceedings{gagnonAutomatedTextClassification2011,
title = {Automated Text Classification based on an ontology standard: Application of the Extensible Business Reporting Language (XBRL) to Reuters Corpus Volume 1 (RCV1) [Classification automatique de textes basée sur une ontologie normée: Application du Extensible Business Reporting Language (XBRL) au Reuters Corpus Volume 1 (RCV1)]},
author = {S. Gagnon and S. Messaoudi and A. Charbonneau},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869423599&partnerID=40&md5=1d12a436b5acb9f715fd6f1669e37be4},
isbn = {978-2-35768-024-1},
year = {2011},
date = {2011-01-01},
booktitle = {CORIA 2011: COnference en Recherche d'Information et Applications - Conference on Information Retrieval and Applications},
pages = {151–158},
address = {Avignon},
abstract = {We demonstrate that applying a domain-specific ontology standard significantly improves Automated Text Classification (ATC). We use the Extensible Business Reporting Language (XBRL) to define a standard ontology and compare the performance of an ACT engine (IBM Classification Module v.8.6) against 2 other list of concepts, namely simple and hierarchical. Our sample of financial news is extracted from the Reuters Corpus Volume 1 (RCV1), where 2 experts in finance help us code 1000 of the 45000 news dealing with mergers and acquisitions. We report recall, precision, the F measure, and in addition a hierarchical measure adjusted for classification relevance in parent classes, as well as a more detailed measure evaluating the classification improvements at the level of each text.},
keywords = {Administrative data processing, Automation, Classification (of information), Domain-specific ontologies, Extensible Business Reporting Language (XBRL), F measure, Financial news, Information retrieval, Ontology, Reuters, Text classification, Text processing},
pubstate = {published},
tppubtype = {inproceedings}
}
We demonstrate that applying a domain-specific ontology standard significantly improves Automated Text Classification (ATC). We use the Extensible Business Reporting Language (XBRL) to define a standard ontology and compare the performance of an ACT engine (IBM Classification Module v.8.6) against 2 other list of concepts, namely simple and hierarchical. Our sample of financial news is extracted from the Reuters Corpus Volume 1 (RCV1), where 2 experts in finance help us code 1000 of the 45000 news dealing with mergers and acquisitions. We report recall, precision, the F measure, and in addition a hierarchical measure adjusted for classification relevance in parent classes, as well as a more detailed measure evaluating the classification improvements at the level of each text.