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Multilingual Dataset for Named Entity Recognition, Entity Linking and Stance Detection in Historical Newspapers ...
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Multilingual Dataset for Named Entity Recognition, Entity Linking and Stance Detection in Historical Newspapers ...
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Entity Linking for Historical Documents: Challenges and Solutions
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Pontes, Elvys Linhares; Cabrera-Diego, Luis Adrián; Moreno, José G.; Boros, Emanuela; Pontes, Elvys,; Hamdi, Ahmed; Sidère, Nicolas; Coustaty, Mickaël; Doucet, Antoine
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In: 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020 ; https://hal.archives-ouvertes.fr/hal-03034492 ; 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, 12504, Springer, pp.215-231, 2020, Lecture Notes in Computer Science, 978-3-030-64452-9. ⟨10.1007/978-3-030-64452-9_19⟩ (2020)
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Abstract:
International audience ; Named entities (NEs) are among the most relevant type of information that can be used to efficiently index and retrieve digital documents. Furthermore, the use of Entity Linking (EL) to disambiguate and relate NEs to knowledge bases, provides supplementary information which can be useful to differentiate ambiguous elements such as geographical locations and peoples' names. In historical documents, the detection and disambiguation of NEs is a challenge. Most historical documents are converted into plain text using an optical character recognition (OCR) system at the expense of some noise. Documents in digital libraries will, therefore, be indexed with errors that may hinder their accessibility. OCR errors affect not only document indexing but the detection, disambiguation, and linking of NEs. This paper aims at analysing the performance of different EL approaches on two multilingual historical corpora, CLEF HIPE 2020 (English, French, German) and NewsEye (Finnish, French, German, Swedish), while proposes several techniques for alleviating the impact of historical data problems on the EL task. Our findings indicate that the proposed approaches not only outperform the baseline in both corpora but additionally they considerably reduce the impact of historical document issues on different subjects and languages.
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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; Deep learning; Digital libraries; Entity linking; Historical data
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URL: https://doi.org/10.1007/978-3-030-64452-9_19
https://hal.archives-ouvertes.fr/hal-03034492/document
https://hal.archives-ouvertes.fr/hal-03034492/file/ICADL_2020___12_14_pages___references.pdf
https://hal.archives-ouvertes.fr/hal-03034492
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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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