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1.761 a 1.770 de 1.780 Resultados

Reproduction_protocol.pdf 18 dic 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
ubner_public_experiments.gz.cpt 18 dic 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,...
RepLab Summarization Dataset 17 jul 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 Summar... Dataset recolectado desde DataCite con autores de la UNED. El enlace le llevará directamente a los datos originales en dicho archivo.
Word similarity benchmarks of recent word embedding models and ontology-based semantic similarity measures 31 oct 2019 Lastra-Díaz, Juan J.; Goikoetxea, Josu; Hadj Taieb, Mohamed Ali; Garcia-Serrano, Ana M.; Ben Aouicha, Mohamed; Agirre, Eneko, 2019, "Word similarity benchmarks of recent word embedding models and ontology-based semantic similarity measures", https://doi.org/10.21950/AQ1CVX, e-cienciaDatos, V2 This dataset is a companion reproducibility package of the related paper submitted for publication, whose aim is to allow the exact replication of a very large experimental survey on word similarity between the families of ontology-based semantic similarity measures and word embe...
embeddings_vs_ontomeasures_final_tables.R 31 oct 2019 - Word similarity benchmarks of recent word embedding models and ontology-based semantic similarity measures Sintasis R - 46,5 KB - MD5: db5cf18c43c60bc151793ea3d62db470 Datos
appendix-reproducible-experiments.pdf 29 ene 2019 - Word similarity benchmarks of recent word embedding models and ontology-based semantic similarity measures Adobe PDF - 278,8 KB - MD5: c35e28086040251b163c5960c7a0c53b
benchmark_survey.exp 29 ene 2019 - Word similarity benchmarks of recent word embedding models and ontology-based semantic similarity measures Desconocido - 53,0 KB - MD5: 1dbd8b637fbc07ff760ed182692a07ab
embeddings_vs_ontomeasures_final_tables.R 29 ene 2019 - Word similarity benchmarks of recent word embedding models and ontology-based semantic similarity measures Sintasis R - 40,8 KB - MD5: 3703d2cae12ce8dad349bb0862156abb
processed_output_benchmarks.zip 29 ene 2019 - Word similarity benchmarks of recent word embedding models and ontology-based semantic similarity measures Archivo ZIP - 34,7 KB - MD5: 11702bf12e3f897fc26299b574b09b0d
raw_output_benchmark_all_datasets.zip 29 ene 2019 - Word similarity benchmarks of recent word embedding models and ontology-based semantic similarity measures Archivo ZIP - 1,8 MB - MD5: 5025a45eec7957f6efab7d85adb6bda2

Reproduction_protocol.pdf

18 dic 2020 - Reproducible experiments on the master thesis: An experimental survey of Named Entity Recognition methods in the biomedical domain

Adobe PDF - 163,8 KB -

ubner_public_experiments.gz.cpt

18 dic 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 -

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,...

RepLab Summarization Dataset

17 jul 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 Summar...

Dataset recolectado desde DataCite con autores de la UNED. El enlace le llevará directamente a los datos originales en dicho archivo.

Word similarity benchmarks of recent word embedding models and ontology-based semantic similarity measures

31 oct 2019

Lastra-Díaz, Juan J.; Goikoetxea, Josu; Hadj Taieb, Mohamed Ali; Garcia-Serrano, Ana M.; Ben Aouicha, Mohamed; Agirre, Eneko, 2019, "Word similarity benchmarks of recent word embedding models and ontology-based semantic similarity measures", https://doi.org/10.21950/AQ1CVX, e-cienciaDatos, V2

This dataset is a companion reproducibility package of the related paper submitted for publication, whose aim is to allow the exact replication of a very large experimental survey on word similarity between the families of ontology-based semantic similarity measures and word embe...

embeddings_vs_ontomeasures_final_tables.R

31 oct 2019 - Word similarity benchmarks of recent word embedding models and ontology-based semantic similarity measures

Sintasis R - 46,5 KB -

Datos

appendix-reproducible-experiments.pdf

29 ene 2019 - Word similarity benchmarks of recent word embedding models and ontology-based semantic similarity measures

Adobe PDF - 278,8 KB -

benchmark_survey.exp

29 ene 2019 - Word similarity benchmarks of recent word embedding models and ontology-based semantic similarity measures

Desconocido - 53,0 KB -

embeddings_vs_ontomeasures_final_tables.R

29 ene 2019 - Word similarity benchmarks of recent word embedding models and ontology-based semantic similarity measures