Gold standard, multi-genre dataset for named entity recognition and linking
Abstract In our study, we introduce a new dataset designed for the evaluation of entity-linking systems. Entity Linking (EL) involves identifying specific segments in a text so-called mentions and linking them to relevant entries in an external Knowledge Base (KB). EL is a challenging task with nume...
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| Format: | Article |
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Nature Portfolio
2025-06-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05274-4 |
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| author | Szymon Olewniczak Julian Szymański |
| author_facet | Szymon Olewniczak Julian Szymański |
| author_sort | Szymon Olewniczak |
| collection | DOAJ |
| description | Abstract In our study, we introduce a new dataset designed for the evaluation of entity-linking systems. Entity Linking (EL) involves identifying specific segments in a text so-called mentions and linking them to relevant entries in an external Knowledge Base (KB). EL is a challenging task with numerous complexities, making it vital to have access to high-quality data for testing. Our dataset is unique in that it encompasses texts from various domains, contrasting with the common focus on single domains, such as newspaper news, in most current datasets. Furthermore, we have annotated each identified text segment with its corresponding entity type, enhancing the dataset’s usefulness and reliability. This dataset employs Wikipedia as its Knowledge Base, which is the prevalent choice for general domain entity linking systems. The dataset is available to download from https://doi.org/10.34808/f3q9-9k64 . |
| format | Article |
| id | doaj-art-70ef9ff26edf421a9fc3510dacb3874d |
| institution | OA Journals |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-70ef9ff26edf421a9fc3510dacb3874d2025-08-20T02:06:23ZengNature PortfolioScientific Data2052-44632025-06-0112112210.1038/s41597-025-05274-4Gold standard, multi-genre dataset for named entity recognition and linkingSzymon Olewniczak0Julian Szymański1Department of Computer Architecture, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of TechnologyDepartment of Computer Architecture, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of TechnologyAbstract In our study, we introduce a new dataset designed for the evaluation of entity-linking systems. Entity Linking (EL) involves identifying specific segments in a text so-called mentions and linking them to relevant entries in an external Knowledge Base (KB). EL is a challenging task with numerous complexities, making it vital to have access to high-quality data for testing. Our dataset is unique in that it encompasses texts from various domains, contrasting with the common focus on single domains, such as newspaper news, in most current datasets. Furthermore, we have annotated each identified text segment with its corresponding entity type, enhancing the dataset’s usefulness and reliability. This dataset employs Wikipedia as its Knowledge Base, which is the prevalent choice for general domain entity linking systems. The dataset is available to download from https://doi.org/10.34808/f3q9-9k64 .https://doi.org/10.1038/s41597-025-05274-4 |
| spellingShingle | Szymon Olewniczak Julian Szymański Gold standard, multi-genre dataset for named entity recognition and linking Scientific Data |
| title | Gold standard, multi-genre dataset for named entity recognition and linking |
| title_full | Gold standard, multi-genre dataset for named entity recognition and linking |
| title_fullStr | Gold standard, multi-genre dataset for named entity recognition and linking |
| title_full_unstemmed | Gold standard, multi-genre dataset for named entity recognition and linking |
| title_short | Gold standard, multi-genre dataset for named entity recognition and linking |
| title_sort | gold standard multi genre dataset for named entity recognition and linking |
| url | https://doi.org/10.1038/s41597-025-05274-4 |
| work_keys_str_mv | AT szymonolewniczak goldstandardmultigenredatasetfornamedentityrecognitionandlinking AT julianszymanski goldstandardmultigenredatasetfornamedentityrecognitionandlinking |