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Extraction of Relation Descriptors for Portuguese Using Conditional Random Fields

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Book cover Advances in Artificial Intelligence -- IBERAMIA 2014 (IBERAMIA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8864))

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Abstract

An important task in Information Extraction is Relation Extraction. Relation Extraction (RE) is the task of detecting and characterizing the semantic relations between entities in the text. This work proposes a new process for the extraction of any relation descriptors between Named Entities (NEs) in the Organization domain, for the Portuguese language, using the Conditional Random Fields (CRF) model. For example, from the following sentence fragment “Microsoft headquartered in Redmond, […]”, we can extract the relation descriptor “headquartered-in”, that relates the NEs “Microsoft” and “Redmond”. We evaluated different features configurations for CRF; the best results were obtained with the inclusion of the semantic feature based on the NE category, since this feature could express, in a better way, the kind of relationship between the pair of NEs we want to identify. The proposed process achieved F-measure rates of 45 % and 53 %, considering the extraction of complete and partial matching, respectively.

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Authors and Affiliations

  1. Pontifícia Universidade Católica do Rio Grande do Sul - PUCRS, Faculdade de Informática, Av. Ipiranga, Porto Alegre, RS, 6681, Brazil

    Sandra Collovini, Lucas Pugens, Aline A. Vanin & Renata Vieira

Authors
  1. Sandra Collovini
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  2. Lucas Pugens
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  3. Aline A. Vanin
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  4. Renata Vieira
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Correspondence to Sandra Collovini .

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Editors and Affiliations

  1. Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

    Ana L.C. Bazzan

  2. Pontifica Universidad Católica (PUC), Santiago de Chile, Chile

    Karim Pichara

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Collovini, S., Pugens, L., Vanin, A.A., Vieira, R. (2014). Extraction of Relation Descriptors for Portuguese Using Conditional Random Fields. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_9

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  • DOI: https://doi.org/10.1007/978-3-319-12027-0_9

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  • Online ISBN: 978-3-319-12027-0

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