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Biomedical text mining applied to document retrieval and semantic indexing

  • Anália Lourenço
  • , Sónia Carneiro
  • , Eugénio C. Ferreira
  • , Rafael Carreira
  • , Luis M. Rocha
  • , Daniel Glez-Peña
  • , José R. Méndez
  • , Florentino Fdez-Riverola
  • , Fernando Diaz
  • , Isabel Rocha
  • , Miguel Rocha

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In Biomedical research, the ability to retrieve the adequate information from the ever growing literature is an extremely important asset. This work provides an enhanced and general purpose approach to the process of document retrieval that enables the filtering of PubMed query results. The system is based on semantic indexing providing, for each set of retrieved documents, a network that links documents and relevant terms obtained by the annotation of biological entities (e.g. genes or proteins). This network provides distinct user perspectives and allows navigation over documents with similar terms and is also used to assess document relevance. A network learning procedure, based on previous work from e-mail spam filtering, is proposed, receiving as input a training set of manually classified documents.

Original languageEnglish
Title of host publicationDistributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, Ambient Assisted Living - 10th Int. Work-Conf. Artificial Neural Networks, IWANN 2009 Workshops, Proceedings
Pages954-963
Number of pages10
EditionPART 2
DOIs
StatePublished - 2009
Event10th International Work-Conference on Artificial Neural Networks, IWANN 2009 - Salamanca, Spain
Duration: Jun 10 2009Jun 12 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5518 LNCS

Conference

Conference10th International Work-Conference on Artificial Neural Networks, IWANN 2009
Country/TerritorySpain
CitySalamanca
Period06/10/0906/12/09

Keywords

  • Biomedical document retrieval
  • Document relevance
  • Enhanced instance retrieval network
  • Named entity recognition
  • Semantic indexing document network

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