DE eng

Search in the Catalogues and Directories

Page: 1 2
Hits 1 – 20 of 24

1
Atténuer les erreurs de numérisation dans la reconnaissance d'entités nommées pour les documents historiques
In: Conférence en Recherche d'Informations et Applications (CORIA 2021) ; https://hal.archives-ouvertes.fr/hal-03320332 ; Conférence en Recherche d'Informations et Applications (CORIA 2021), ARIA : Association Francophone de Recherche d’Information (RI) et Applications, Apr 2021, Grenoble (virtuel), France. pp.1 - 7 ; http://coria.asso-aria.org/2021/articles/mini_24/main.pdf (2021)
BASE
Show details
2
A Multilingual Dataset for Named Entity Recognition, Entity Linking and Stance Detection in Historical Newspapers
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)
BASE
Show details
3
MELHISSA: a multilingual entity linking architecture for historical press articles ...
BASE
Show details
4
MELHISSA: a multilingual entity linking architecture for historical press articles ...
BASE
Show details
5
Multilingual Dataset for Named Entity Recognition, Entity Linking and Stance Detection in Historical Newspapers ...
BASE
Show details
6
A Multilingual Dataset for Named Entity Recognition, Entity Linking and Stance Detection in Historical Newspapers ...
BASE
Show details
7
Annotation Guidelines for Named Entity Recognition, Entity Linking and Stance Detection ...
BASE
Show details
8
Multilingual Dataset for Named Entity Recognition, Entity Linking and Stance Detection in Historical Newspapers ...
BASE
Show details
9
Annotation Guidelines for Named Entity Recognition, Entity Linking and Stance Detection ...
BASE
Show details
10
A Multilingual Dataset for Named Entity Recognition, Entity Linking and Stance Detection in Historical Newspapers ...
BASE
Show details
11
Entity Linking for Historical Documents: Challenges and Solutions
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)
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.
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
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
BASE
Hide details
12
Robust Named Entity Recognition and Linking on Historical Multilingual Documents
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)
BASE
Show details
13
Robust Named Entity Recognition and Linking on Historical Multilingual Documents ...
BASE
Show details
14
Robust Named Entity Recognition and Linking on Historical Multilingual Documents ...
BASE
Show details
15
Benchmark for the evaluation of named entity recognition over ancient documents ...
BASE
Show details
16
Robust Named Entity Recognition and Linking on Historical Multilingual Documents ...
BASE
Show details
17
Benchmark for the evaluation of named entity recognition over ancient documents ...
BASE
Show details
18
Robust Named Entity Recognition and Linking on Historical Multilingual Documents ...
BASE
Show details
19
Alleviating Digitization Errors in Named Entity Recognition for Historical Documents ...
BASE
Show details
20
Alleviating Digitization Errors in Named Entity Recognition for Historical Documents ...
BASE
Show details

Page: 1 2

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
24
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern