3,591 to 3,600 of 3,617 Results
Adobe PDF - 437,8 KB -
MD5: 352489b0a4478adbde75ca4790159e7b
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application/mac-compactpro - 66,0 GB -
MD5: cf5d23e515313d35a1a3d959e0140ed9
In order to obtain a decrypt password for this, you should sign and obtain a license for the National Library of Medicine (NLM) of the United States to use the UMLS Metathesaurus databases, as well as SNOMED-CT and MeSH ontologies included in this Docker image. For this purpose, you should go top the NLM license page, https://uts.nlm.nih.gov//licen... |
Archivo Gzip - 573,0 KB -
MD5: f21249e966162907d8c08edeb7f1851a
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Archivo Gzip - 573,5 KB -
MD5: 53086a1feda2458b97dfad161fc256f5
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Dec 23, 2020
Toscano, Maurizio; Aitor Díaz, 2020, "Mapping digital humanities in Spain - 1993-2019", https://doi.org/10.5281/ZENODO.3893545
Mapping digital humanities in Spain (1993-2019) This dataset has been extensively analysed in the following paper https://doi.org/10.3145/epi.2020.nov.01 and has also been used for the following poster https://doi.org/10.5281/zenodo.4256689Dataset recolectado desde DataCite con autores de la UNED. El enlace le llevará directamente a los datos originales en dicho archivo. |
Dec 18, 2020
Hennig, Sebastian; Garcia-Serrano, Ana M., 2020, "Reproducible experiments on the master thesis: An experimental survey of Named Entity Recognition methods in the biomedical domain", https://doi.org/10.21950/DYAZRE, e-cienciaDatos, V1
Semantic Textual Similarity (also known as Semantic Short-text Similarity) is a research problem that aims to calculate the similarity among text units (phrases, sentences, paragraphs or texts) focusing on the semantic content. The importance of Semantic Similarity in Natural Language Processing has increased in the last years due to its relevance... |
Dec 18, 2020 -
Reproducible experiments on the master thesis: An experimental survey of Named Entity Recognition methods in the biomedical domain
Texto plano - 6,1 KB -
MD5: 1dd80692aac37dccc56aa55ef2086581
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Dec 18, 2020 -
Reproducible experiments on the master thesis: An experimental survey of Named Entity Recognition methods in the biomedical domain
Adobe PDF - 163,8 KB -
MD5: ceec87c68a5dc590ba4ad1dca2709bc6
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Dec 18, 2020 -
Reproducible experiments on the master thesis: An experimental survey of Named Entity Recognition methods in the biomedical domain
application/mac-compactpro - 6,8 GB -
MD5: 63c928b1042bb5c9ddfab647d5c1c492
In order to obtain a decrypt password for this, you should sign and obtain a license for the National Library of Medicine (NLM) of the United States to use the UMLS Metathesaurus databases, as well as SNOMED-CT and MeSH ontologies included in this Docker image. For this purpose, you should go top the NLM license page, https://uts.nlm.nih.gov//licen... |
Jul 17, 2020
Rodríguez-Vidal, Javier; Carrillo-de-Albornoz, Jorge; Amigó, Enrique; Plaza, Laura; Gonzalo, Julio; Verdejo, Felisa, 2019, "RepLab Summarization Dataset", https://doi.org/10.5281/ZENODO.2536800
RepLab Summarization Dataset This package contains the dataset generated in the research published in the paper: "Javier Rodríguez-Vidal, Jorge Carrillo-de-Albornoz, Enrique Amigó, Laura Plaza, Julio Gonzalo and Felisa Verdejo. 2019. Automatic Generation of Entity-Oriented Summaries for Reputation Management. Ambient Intelligence & Humanized Comput...Dataset recolectado desde DataCite con autores de la UNED. El enlace le llevará directamente a los datos originales en dicho archivo. |
