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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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Gado2: multilingual newspapers from the Netherlands Indies ...
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Gado2: multilingual newspapers from the Netherlands Indies ...
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Multilingual Dataset for Named Entity Recognition, Entity Linking and Stance Detection in Historical Newspapers ...
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Annotation Guidelines for Named Entity Recognition, Entity Linking and Stance Detection ...
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Gado2: multilingual newspapers from the Netherlands Indies ...
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Multilingual Dataset for Named Entity Recognition, Entity Linking and Stance Detection in Historical Newspapers ...
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Annotation Guidelines for Named Entity Recognition, Entity Linking and Stance Detection ...
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Gado2: multilingual newspapers from the Netherlands Indies ...
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Robust Named Entity Recognition and Linking on Historical Multilingual Documents
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In: Conference and Labs of the Evaluation Forum (CLEF 2020) ; https://hal.archives-ouvertes.fr/hal-03026969 ; Conference and Labs of the Evaluation Forum (CLEF 2020), Sep 2020, Thessaloniki, Greece. pp.1-17, ⟨10.5281/zenodo.4068074⟩ ; http://ceur-ws.org/Vol-2696/paper_171.pdf (2020)
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Abstract:
International audience ; This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the Identifying Historical People, Places, and other Entities (HIPE) evaluation campaign of CLEF 2020. Our participation relies on two neural models, one for named entity recognition and classification (NERC) and another one for entity linking (EL). We carefully pre-processed inputs to mitigate its flaws, notably in terms of segmentation. Our submitted runs cover all languages (English, French, and German) and sub-tasks proposed in the lab: NERC, endto-end EL, and EL-only. Our submissions obtained top performance in 50 out of the 52 scoreboards proposed by the lab organizers. In further detail, out of 70 runs submitted by 13 participants, our approaches obtained the best score for all metrics in all three languages both for NERC and for end-to-end EL. It also obtained the best score for all metrics in French and German for EL-only.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-DL]Computer Science [cs]/Digital Libraries [cs.DL]; [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC]; [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; Entity linking; Information extraction; Named Entity Recognition
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URL: https://hal.archives-ouvertes.fr/hal-03026969 https://hal.archives-ouvertes.fr/hal-03026969/document https://hal.archives-ouvertes.fr/hal-03026969/file/paper_171-2.pdf https://doi.org/10.5281/zenodo.4068074
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Robust Named Entity Recognition and Linking on Historical Multilingual Documents ...
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Robust Named Entity Recognition and Linking on Historical Multilingual Documents ...
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Linking Named Entities across Languages using Multilingual Word Embeddings ...
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