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dc.contributor.author | Danger Mercaderes, Roxana María | es_ES |
dc.contributor.author | Pla Santamaría, Ferran | es_ES |
dc.contributor.author | Molina Marco, Antonio | es_ES |
dc.contributor.author | Rosso, Paolo | es_ES |
dc.date.accessioned | 2015-03-10T12:31:15Z | |
dc.date.available | 2015-03-10T12:31:15Z | |
dc.date.issued | 2014-02 | |
dc.identifier.issn | 0950-7051 | |
dc.identifier.uri | http://hdl.handle.net/10251/47925 | |
dc.description.abstract | [EN] The majority of biological functions of any living being are related to Protein Protein Interactions (PPI). PPI discoveries are reported in form of research publications whose volume grows day after day. Consequently, automatic PPI information extraction systems are a pressing need for biologists. In this paper we are mainly concerned with the named entity detection module of PPIES (the PPI information extraction system we are implementing) which recognizes twelve entity types relevant in PPI context. It is composed of two sub-modules: a dictionary look-up with extensive normalization and acronym detection, and a Conditional Random Field classifier. The dictionary look-up module has been tested with Interaction Method Task (IMT), and it improves by approximately 10% the current solutions that do not use Machine Learning (ML). The second module has been used to create a classifier using the Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA 04) data set. It does not use any external resources, or complex or ad hoc post-processing, and obtains 77.25%, 75.04% and 76.13 for precision, recall, and F1-measure, respectively, improving all previous results obtained for this data set. | es_ES |
dc.description.sponsorship | This work has been funded by MICINN, Spain, as part of the "Juan de la Cierva" Program and the Project DIANA-Applications (TIN2012-38603-C02-01), as well as the by the European Commission as part of the WIQ-EI IRSES Project (Grant No. 269180) within the FP 7 Marie Curie People Framework. | en_EN |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Knowledge-Based Systems | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Biomedical named entity recognition | es_ES |
dc.subject | Protein-Protein interactions | es_ES |
dc.subject | Dictionary look-up | es_ES |
dc.subject | Conditional random field | es_ES |
dc.subject | Support vector machine | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | Towards a Protein-Protein Interaction information extraction system: recognizing named entities | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.knosys.2013.12.010 | |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/FP7/269180/EU/Web Information Quality Evaluation Initiative/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//TIN2012-38603-C02-01/ES/DIANA-APPLICATIONS: FINDING HIDDEN KNOWLEDGE IN TEXTS: APPLICATIONS/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.description.bibliographicCitation | Danger Mercaderes, RM.; Pla Santamaría, F.; Molina Marco, A.; Rosso, P. (2014). Towards a Protein-Protein Interaction information extraction system: recognizing named entities. Knowledge-Based Systems. 57:104-118. https://doi.org/10.1016/j.knosys.2013.12.010 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1016/j.knosys.2013.12.010 | es_ES |
dc.description.upvformatpinicio | 104 | es_ES |
dc.description.upvformatpfin | 118 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 57 | es_ES |
dc.relation.senia | 277919 | |
dc.contributor.funder | European Commission | |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |