Publicação
NER in archival finding aids: extended
| Resumo: | 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. |
|---|---|
| Autores principais: | Cunha, Luís Filipe da Costa |
| Outros Autores: | Ramalho, José Carlos |
| Assunto: | named entity recognition archival search aids machine learning deep learning maximum entropy |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1866877396172406784 |
|---|---|
| author | Cunha, Luís Filipe da Costa |
| author2 | Ramalho, José Carlos |
| author2_role | author |
| author_facet | Cunha, Luís Filipe da Costa Ramalho, José Carlos |
| author_role | author |
| contributor_name_str_mv | Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Cunha, Luís Filipe da Costa\"},{\"Person.name\":\"Ramalho, José Carlos\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Cunha, Luís Filipe da Costa Ramalho, José Carlos |
| datacite.date.Accepted.fl_str_mv | 2022-01-17T00:00:00Z |
| datacite.date.available.fl_str_mv | 2022-03-29T11:56:39Z |
| datacite.date.embargoed.fl_str_mv | 2022-03-29T11:56:39Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | named entity recognition archival search aids machine learning deep learning maximum entropy |
| datacite.titles.title.fl_str_mv | NER in archival finding aids: extended |
| dc.contributor.none.fl_str_mv | Universidade do Minho |
| dc.creator.none.fl_str_mv | Cunha, Luís Filipe da Costa Ramalho, José Carlos |
| dc.date.Accepted.fl_str_mv | 2022-01-17T00:00:00Z |
| dc.date.available.fl_str_mv | 2022-03-29T11:56:39Z |
| dc.date.embargoed.fl_str_mv | 2022-03-29T11:56:39Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/76687 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Multidisciplinary Digital Publishing Institute |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.rights.rights.copyright.fl_str_mv | openAccess |
| dc.subject.none.fl_str_mv | named entity recognition archival search aids machine learning deep learning maximum entropy |
| dc.title.fl_str_mv | NER in archival finding aids: extended |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| 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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://prod-dspace.uminho.pt/bitstreams/5cca2c0f-23ec-4175-8b9b-71dfe8a88006/download |
| id | rum_3bf4d4d97acd57eaeff6a6cccbb4f549 |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/76687 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/76687 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Cunha, Luís Filipe da Costa Ramalho, José Carlos |
| publishDate | 2022 |
| publisher.none.fl_str_mv | Multidisciplinary Digital Publishing Institute |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engMultidisciplinary Digital Publishing InstituteporThe 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.application/pdfengNER in archival finding aids: extendedCunha, Luís Filipe da CostaRamalho, José CarlosHostingInstitutionOrganizationalUniversidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptDOIIsPartOf10.3390/make40100032022-03-29T11:56:39Z2022-01-172022-03-24T14:47:06Z2022-01-17T00:00:00ZHandlehttps://hdl.handle.net/1822/76687http://purl.org/coar/access_right/c_abf2open accessnamed entity recognitionarchival search aidsmachine learningdeep learningmaximum entropy1728733 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal article2022-01-17http://creativecommons.org/licenses/by/4.0/openAccesshttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://prod-dspace.uminho.pt/bitstreams/5cca2c0f-23ec-4175-8b9b-71dfe8a88006/download |
| spellingShingle | NER in archival finding aids: extended Cunha, Luís Filipe da Costa named entity recognition archival search aids machine learning deep learning maximum entropy |
| status | SINGLETON |
| subject.fl_str_mv | 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 |
| topic_facet | named entity recognition archival search aids machine learning deep learning maximum entropy |
| url | https://hdl.handle.net/1822/76687 |
| visible | 1 |