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| 8. |  | MOURA, M. F.; MACACINI, R. M.; REZENDE, S. O. Easily labelling hierarchical document clusters. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS, 23.; SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SOFTWARE, 22.; WORKSHOP EM ALGORITMOS E APLICAÇÕES DE MINERAÇÃO DE DADOS, 4., 2008, Campinas. Anais... Campinas: UNICAMP, Instituto de Computação, 2008. p. 37-45.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 19. | | RODRIGUES, L. S.; SINOARA, R. A.; REZENDE, S. O.; MARCACINI, R. M.; MOURA, M. F. Identificação de Pontos Perceptualmente Importantes (PIP) em séries temporais de tópicos extraídos de dados textuais. In: MOSTRA DE ESTAGIÁRIOS E BOLSISTAS DA EMBRAPA INFORMÁTICA AGROPECUÁRIA, 11., 2015, Campinas. Resumos expandidos... Brasília, DF: Embrapa, 2015. p. 38-44.| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 41 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
06/02/2013 |
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Data da última atualização: |
22/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
VARGAS, R. N. P.; MOURA, M. F.; SPERANZA, E. A.; RODRIGUEZ, E.; REZENDE, S. O. |
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Afiliação: |
ROSA NATHALIE PORTUGAL VARGAS, ICMC/USP; MARIA FERNANDA MOURA, CNPTIA; EDUARDO ANTONIO SPERANZA, CNPTIA; ERCILIA RODRIGUEZ; SOLANGE OLIVEIRA REZENDE, ICMC/USP. |
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Título: |
The SpatialCIM methodology for spatial document coverage disambiguation and the entity recognition process aided by linguistic techniques. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
In: GEOSPATIAL INFORMATION AND DOCUMENTS; PACIFIC-ASIA CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 16., 2012, Kuala Lumpur. Workshop... [S.l.: s.n.], 2012. |
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Páginas: |
Não paginado. |
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Idioma: |
Inglês |
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Notas: |
GeoDoc 2012, PAKDD 2012. |
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Conteúdo: |
Abstract. Nowadays it is becoming more usual for users to take into account the geographical localization of the documents in the retrieval information process. However, the conventional retrieval information systems based on key-word matching do not consider which words can represent geographical entities that are spatially related to other entities in the document. This paper presents the SpatialCIM methodology, which is based on three steps: pre-processing, data expansion and disambiguation. In the pre-processing step, the entity recognition process is carried out with the support of the Rembrandt tool. Additionally, a comparison between the performances regarding the discovery of the location entities in the texts of the Rembrandt tool against the use of a controlled vocabulary corresponding to the Brazilian geographic locations are presented. For the comparison a set of geographic labeled news covering the sugar cane culture in the Portuguese language is used. The results showed a F-measure value increase for the Rembrandt tool from 45% in the non-disambiguated process to 0.50 after disambiguation and from 35% to 38% using the controlled vocabulary. Additionally, the results showed the Rembrandt tool has a minimal amplitude difference between precision and recall, although the controlled vocabulary has always the biggest recall values. |
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Palavras-Chave: |
Ambiguity Problem; Metodologia SpatialCIM; Named Entity Recognition and Classification; Problema de ambiguidade; Toponym resolution. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/76097/1/SpatialCIM.pdf
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Marc: |
LEADER 02292nam a2200241 a 4500
001 1948462
005 2020-01-22
008 2012 bl uuuu u00u1 u #d
100 1 $aVARGAS, R. N. P.
245 $aThe SpatialCIM methodology for spatial document coverage disambiguation and the entity recognition process aided by linguistic techniques.$h[electronic resource]
260 $aIn: GEOSPATIAL INFORMATION AND DOCUMENTS; PACIFIC-ASIA CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 16., 2012, Kuala Lumpur. Workshop... [S.l.: s.n.]$c2012
300 $aNão paginado.
500 $aGeoDoc 2012, PAKDD 2012.
520 $aAbstract. Nowadays it is becoming more usual for users to take into account the geographical localization of the documents in the retrieval information process. However, the conventional retrieval information systems based on key-word matching do not consider which words can represent geographical entities that are spatially related to other entities in the document. This paper presents the SpatialCIM methodology, which is based on three steps: pre-processing, data expansion and disambiguation. In the pre-processing step, the entity recognition process is carried out with the support of the Rembrandt tool. Additionally, a comparison between the performances regarding the discovery of the location entities in the texts of the Rembrandt tool against the use of a controlled vocabulary corresponding to the Brazilian geographic locations are presented. For the comparison a set of geographic labeled news covering the sugar cane culture in the Portuguese language is used. The results showed a F-measure value increase for the Rembrandt tool from 45% in the non-disambiguated process to 0.50 after disambiguation and from 35% to 38% using the controlled vocabulary. Additionally, the results showed the Rembrandt tool has a minimal amplitude difference between precision and recall, although the controlled vocabulary has always the biggest recall values.
653 $aAmbiguity Problem
653 $aMetodologia SpatialCIM
653 $aNamed Entity Recognition and Classification
653 $aProblema de ambiguidade
653 $aToponym resolution
700 1 $aMOURA, M. F.
700 1 $aSPERANZA, E. A.
700 1 $aRODRIGUEZ, E.
700 1 $aREZENDE, S. O.
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Embrapa Agricultura Digital (CNPTIA) |
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