NER in Archival Finding Aids: Extended
The amount of information preserved in Portuguese archives has increased over the years. These documents represent a national heritage of high importance, as they portray the country’s history. Currently, most Portuguese archives have made their finding aids available to the public in digital format...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
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| Series: | Machine Learning and Knowledge Extraction |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-4990/4/1/3 |
| _version_ | 1797445817918816256 |
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| author | Luís Filipe da Costa Cunha José Carlos Ramalho |
| author_facet | Luís Filipe da Costa Cunha José Carlos Ramalho |
| author_sort | Luís Filipe da Costa Cunha |
| collection | DOAJ |
| description | The amount of information preserved in Portuguese archives has increased over the years. These documents represent a national heritage of high importance, as they portray the country’s history. Currently, most Portuguese archives have made their finding aids available to the public in digital format, however, these data do not have any annotation, so it is not always easy to analyze their content. In this work, Named Entity Recognition solutions were created that allow the identification and classification of several named entities from the archival finding aids. These named entities translate into crucial information about their context and, with high confidence results, they can be used for several purposes, for example, the creation of smart browsing tools by using entity linking and record linking techniques. In order to achieve high result scores, we annotated several corpora to train our own Machine Learning algorithms in this context domain. We also used different architectures, such as CNNs, LSTMs, and Maximum Entropy models. Finally, all the created datasets and ML models were made available to the public with a developed web platform, NER@DI. |
| first_indexed | 2024-03-09T13:32:15Z |
| format | Article |
| id | doaj.art-9383b91b50d44052a98644ca842f77e7 |
| institution | Directory Open Access Journal |
| issn | 2504-4990 |
| language | English |
| last_indexed | 2024-03-09T13:32:15Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj.art-9383b91b50d44052a98644ca842f77e72023-11-30T21:16:49ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902022-01-0141426510.3390/make4010003NER in Archival Finding Aids: ExtendedLuís Filipe da Costa Cunha0José Carlos Ramalho1Department of Informatics, University of Minho, 4710-057 Braga, PortugalDepartment of Informatics, University of Minho, 4710-057 Braga, PortugalThe amount of information preserved in Portuguese archives has increased over the years. These documents represent a national heritage of high importance, as they portray the country’s history. Currently, most Portuguese archives have made their finding aids available to the public in digital format, however, these data do not have any annotation, so it is not always easy to analyze their content. In this work, Named Entity Recognition solutions were created that allow the identification and classification of several named entities from the archival finding aids. These named entities translate into crucial information about their context and, with high confidence results, they can be used for several purposes, for example, the creation of smart browsing tools by using entity linking and record linking techniques. In order to achieve high result scores, we annotated several corpora to train our own Machine Learning algorithms in this context domain. We also used different architectures, such as CNNs, LSTMs, and Maximum Entropy models. Finally, all the created datasets and ML models were made available to the public with a developed web platform, NER@DI.https://www.mdpi.com/2504-4990/4/1/3named entity recognitionarchival search aidsmachine learningdeep learningmaximum entropy |
| spellingShingle | Luís Filipe da Costa Cunha José Carlos Ramalho NER in Archival Finding Aids: Extended Machine Learning and Knowledge Extraction named entity recognition archival search aids machine learning deep learning maximum entropy |
| title | NER in Archival Finding Aids: Extended |
| title_full | NER in Archival Finding Aids: Extended |
| title_fullStr | NER in Archival Finding Aids: Extended |
| title_full_unstemmed | NER in Archival Finding Aids: Extended |
| title_short | NER in Archival Finding Aids: Extended |
| title_sort | ner in archival finding aids extended |
| topic | named entity recognition archival search aids machine learning deep learning maximum entropy |
| url | https://www.mdpi.com/2504-4990/4/1/3 |
| work_keys_str_mv | AT luisfilipedacostacunha nerinarchivalfindingaidsextended AT josecarlosramalho nerinarchivalfindingaidsextended |