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        <title>Combining Deep Learning Models and Lexical Linked Data: Some Insights from the Development of a Multilingual News Named Entity Recognition and Linking Dataset</title>
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    <abstract>This paper presents the methodology and outcomes of a Named Entity Recognition and Linking multilingual news benchmark that leverages both Deep learning approaches by using a fine-tuned transformer model to detect mentions of persons, locations and organisations in text, and Linguistic Linked Open Data, through the use of Wikidata to disambiguate mentions and link them to ontology entries. It shows all the advantages of combining both approaches, not only for building the benchmark but also for fine-tuning detection models. We also insist on several perspectives of research to improve the accuracy of a combining system and go further on leveraging the complementary approaches.</abstract>
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%0 Conference Proceedings
%T Combining Deep Learning Models and Lexical Linked Data: Some Insights from the Development of a Multilingual News Named Entity Recognition and Linking Dataset
%A Cartier, Emmanuel
%A Peetermans, Emile
%Y Sérasset, Gilles
%Y Oliveira, Hugo Gonçalo
%Y Oleskeviciene, Giedre Valunaite
%S Proceedings of the Workshop on Deep Learning and Linked Data (DLnLD) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F cartier-peetermans-2024-combining
%X This paper presents the methodology and outcomes of a Named Entity Recognition and Linking multilingual news benchmark that leverages both Deep learning approaches by using a fine-tuned transformer model to detect mentions of persons, locations and organisations in text, and Linguistic Linked Open Data, through the use of Wikidata to disambiguate mentions and link them to ontology entries. It shows all the advantages of combining both approaches, not only for building the benchmark but also for fine-tuning detection models. We also insist on several perspectives of research to improve the accuracy of a combining system and go further on leveraging the complementary approaches.
%U https://aclanthology.org/2024.dlnld-1.3
%P 31-44

Markdown (Informal)

[Combining Deep Learning Models and Lexical Linked Data: Some Insights from the Development of a Multilingual News Named Entity Recognition and Linking Dataset](https://aclanthology.org/2024.dlnld-1.3) (Cartier & Peetermans, DLnLD-WS 2024)

• Combining Deep Learning Models and Lexical Linked Data: Some Insights from the Development of a Multilingual News Named Entity Recognition and Linking Dataset (Cartier & Peetermans, DLnLD-WS 2024)

ACL

• Emmanuel Cartier and Emile Peetermans. 2024. Combining Deep Learning Models and Lexical Linked Data: Some Insights from the Development of a Multilingual News Named Entity Recognition and Linking Dataset. In Proceedings of the Workshop on Deep Learning and Linked Data (DLnLD) @ LREC-COLING 2024, pages 31–44, Torino, Italia. ELRA and ICCL.