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Assessing the impact of OCR noise on multilingual event detection over digitised documents
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In: ISSN: 1432-5012 ; EISSN: 1432-1300 ; International Journal on Digital Libraries ; https://hal.archives-ouvertes.fr/hal-03635985 ; International Journal on Digital Libraries, Springer Verlag, 2022, ⟨10.1007/s00799-022-00325-2⟩ (2022)
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Computational Measures of Deceptive Language: Prospects and Issues
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In: ISSN: 2297-900X ; EISSN: 2297-900X ; Frontiers in Communication ; https://hal.archives-ouvertes.fr/hal-03629780 ; Frontiers in Communication, Frontiers, 2022, 7, pp.792378. ⟨10.3389/fcomm.2022.792378⟩ (2022)
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Multiword Expression Features for Automatic Hate Speech Detection
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In: NLDB 2021 - 26th International Conference on Natural Language & Information Systems ; https://hal.archives-ouvertes.fr/hal-03231047 ; NLDB 2021 - 26th International Conference on Natural Language & Information Systems, Jun 2021, Saarbrücken/Virtual, Germany ; http://nldb2021.sb.dfki.de/ (2021)
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Hate speech and offensive language detection using transfer learning approaches ; Détection du discours de haine et du langage offensant utilisant des approches de Transfer Learning
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In: https://tel.archives-ouvertes.fr/tel-03276023 ; Document and Text Processing. Institut Polytechnique de Paris, 2021. English. ⟨NNT : 2021IPPAS007⟩ (2021)
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A Multilingual Dataset for Named Entity Recognition, Entity Linking and Stance Detection in Historical Newspapers
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In: SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval ; https://hal.archives-ouvertes.fr/hal-03418387 ; SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 2021, Virtual Event, Canada. pp.2328-2334, ⟨10.1145/3404835.3463255⟩ (2021)
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| 6 |
Impact Analysis of Document Digitization on Event Extraction ...
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Impact Analysis of Document Digitization on Event Extraction ...
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| 8 |
Fine-Grained Implicit Sentiment in Financial News: Uncovering Hidden Bulls and Bears
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In: Electronics ; Volume 10 ; Issue 20 (2021)
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| 9 |
Analyzing Non-Textual Content Elements to Detect Academic Plagiarism
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| 10 |
Cross language plagiarism detection with contextualized word embeddings ; Detecção de plágio multilíngue usando word embeddings contextualizadas
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Abstract:
Plagiarism is the use of someone else’s work without the proper acknowledgment and citation, being one of the most significant publishing issues in academia and science. A study conducted by CopyLeaks in 2020 showed that plagiarism increased by 10% after the transition to online classes during the COVID-19 pandemic. In some cases, authors may translate texts from another language and include them in their work. This more “sophisticated” behavior is known as cross-language plagiarism. In this work, we investigate methods that are used for cross-language plagiarism detection. Although some of the approaches developed until now use word embeddings as part of their pipelines, few explore contextualized word embeddings. Contextualized embeddings can help address fundamental characteristics of language such as polysemy and synonymy by taking into account the context in which a particular word occurs. Pre-trained multilingual models have shown outstanding performance in downstream natural language understanding tasks, such as sentence similarity and next sentence prediction. Motivated by these promising results in tasks related to plagiarism detection, we present a new proposal for cross-language plagiarism detection using pre-trained multilingual models with contextualized embeddings. Experiments performed on different datasets, such as PAN-PC-12, show that the proposed cross-language plagiarism detection using contextualized embeddings outperforms state-of-the-art models by 9% and 11% regarding plagdet results obtained for the English-Spanish and English-German language pairs. ; Plágio é o uso do trabalho de outra pessoa sem o devido reconhecimento e citação, sendo um dos maiores problemas editoriais da academia e da ciência. Um estudo realizado em 2020 pela CopyLeaks mostrou que o plágio aumentou em 10% após a transição para aulas online durante a pandemia da COVID-19. Em alguns casos, os autores podem traduzir textos de outro idioma e incluir em seus próprios trabalhos. Este comportamento mais “sofisticado” é conhecido como plágio multilíngue. Neste trabalho, investigamos métodos que são usados para a detecção do plágio multilíngue. Embora algumas das abordagens desenvolvidas até agora utilizem word embeddings como parte de seu pipeline, poucas delas exploram contexualized word embeddings. Contexualized word embeddings consideram características fundamentais da linguagem, como a polissemia, levando em conta o contexto no qual uma palavra em particular ocorre. Modelos multilíngues pré-treinados têm demonstrado grande desempenho em tarefas multilíngues, tais como similaridade de sentenças e predição de próxima sentença. Assim, com resultados promissores para tarefas relacionadas à detecção de plágio, apresentamos uma nova proposta para a detecção de plágio multilíngue utilizando modelos multilíngues pré-treinados com embeddings contextuais. Experimentos realizados em diferentes conjuntos de dados, como o PAN-PC-12, mostram que a detecção de plágio multilíngue utilizando modelos multilíngues pré-treinados com embeddings contextuais supera supera em 9% e 11% os modelos de última geração em relação aos resultados de plagdet obtidos para os pares de idiomas inglês-espanhol e inglês-alemão.
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Keyword:
BERT; Cross language information retrieval; Cross language plagiarism detection; Plágio; Recuperação de informação : multilíngue; Word embeddings
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URL: http://hdl.handle.net/10183/226141
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| 12 |
Application-Oriented Approach for Detecting Cyberaggression in Social Media
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In: International Conference on Applied Human Factors and Ergonomics ; https://hal.archives-ouvertes.fr/hal-02903422 ; International Conference on Applied Human Factors and Ergonomics, Jul 2020, San Diego, United States. pp.129-136, ⟨10.1007/978-3-030-51328-3_19⟩ ; https://link.springer.com/chapter/10.1007%2F978-3-030-51328-3_19 (2020)
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| 13 |
Affective behavior modeling on social networks ; Modélisation des sentiments sur les réseaux sociaux
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In: https://tel.archives-ouvertes.fr/tel-03339755 ; Social and Information Networks [cs.SI]. Université Montpellier, 2020. English. ⟨NNT : 2020MONTS073⟩ (2020)
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| 14 |
Impact Analysis of Document Digitization on Event Extraction
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In: CEUR Workshop Proceedings ; 4th Workshop on Natural Language for Artificial Intelligence (NL4AI 2020) co-located with the 19th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2020) ; https://hal.archives-ouvertes.fr/hal-03026148 ; 4th Workshop on Natural Language for Artificial Intelligence (NL4AI 2020) co-located with the 19th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2020), Nov 2020, Virtual, Italy. pp.17-28 ; http://sag.art.uniroma2.it/NL4AI/ (2020)
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| 15 |
Detecting deviations from activities of daily living routines using kinect depth maps and power consumption data
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In: Research outputs 2014 to 2021 (2020)
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| 18 |
Sequence Covering for Efficient Host-Based Intrusion Detection
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In: ISSN: 1556-6013 ; IEEE Transactions on Information Forensics and Security ; https://hal.archives-ouvertes.fr/hal-01653650 ; IEEE Transactions on Information Forensics and Security, Institute of Electrical and Electronics Engineers, 2019, 14 (4), pp.994-1006. ⟨10.1109/TIFS.2018.2868614⟩ ; https://ieeexplore.ieee.org/document/8454473 (2019)
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| 19 |
StoryMiner: An Automated and Scalable Framework for Story Analysis and Detection from Social Media
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| 20 |
A novel framework for biomedical entity sense induction
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In: ISSN: 1532-0464 ; EISSN: 1532-0480 ; Journal of Biomedical Informatics ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01851988 ; Journal of Biomedical Informatics, Elsevier, 2018, 84, pp.31-41. ⟨10.1016/j.jbi.2018.06.007⟩ (2018)
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