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Topic recommendation using Doc2Vec
Laboratory of Knowledge and Intelligent Computing, Technological Educational Institute of Epirus, Arta, Greece.
Department of Electrical Engineering and Computer Technology, University of Patras, Greece.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-9701-4203
Laboratory of Knowledge and Intelligent Computing, Technological Educational Institute of Epirus, Arta, Greece.
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The ever-increasing number of electronic content stored in digital libraries requires a significant amount of effort in cataloguing and has led to self-deposit solutions where the authors submit and publish their own digital records. Even in self-deposit, going through the abstract and assigning subject terms or keywords is a time consuming and expensive process, yet crucial for the metadata quality of the record that affects retrieval. Therefore, an automatic, or even a semi-automatic process that can recommend topics for a new entry is of huge practical value. A system that can address that has to rely basically on two components, one component for efficiently representing the relevant information of the new document and one component for recommending an appropriate set of topics based on the representation of the previous stage. In this work, different candidate solutions for both components are investigated and compared. For the first stage both distributed Document to Vector (doc2vec) and conventional Bag of Words (BoW) components are employed, while for the latter two different transformation approaches from the field of multi-label classification are compared. For the comparison, a collection of Ph.D. abstracts (~19000 documents) from the MIT Libraries Dspace repository is used suggesting that different combinations can provide high quality solutions.

Place, publisher, year, edition, pages
2018. article id 8489513
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-71543DOI: 10.1109/IJCNN.2018.8489513Scopus ID: 2-s2.0-85056491792ISBN: 978-1-5090-6014-6 (electronic)OAI: oai:DiVA.org:ltu-71543DiVA, id: diva2:1262483
Conference
2018 International Joint Conference on Neural Networks (IJCNN);8-13 July 2018;Rio de Janeiro, Brazil
Available from: 2018-11-12 Created: 2018-11-12 Last updated: 2019-01-14Bibliographically approved

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Georgoulas, George G.

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf