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dc.contributor.authorKoci, Elvis
dc.contributor.authorThiele, Maik
dc.contributor.authorRomero Moral, Óscar
dc.contributor.authorLehner, Wolfgang
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació
dc.date.accessioned2017-02-06T10:26:10Z
dc.date.available2017-02-06T10:26:10Z
dc.date.issued2016
dc.identifier.citationKoci, E., Thiele, M., Romero, O., Lehner, W. A machine learning approach for layout inference in spreadsheets. A: International Conference on Knowledge Discovery and Information Retrieval. "IC3K 2016: Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management: volume 1: KDIR". Porto: SciTePress, 2016, p. 77-88.
dc.identifier.isbn978-989-758-203-5
dc.identifier.urihttp://hdl.handle.net/2117/100584
dc.description.abstractSpreadsheet applications are one of the most used tools for content generation and presentation in industry and the Web. In spite of this success, there does not exist a comprehensive approach to automatically extract and reuse the richness of data maintained in this format. The biggest obstacle is the lack of awareness about the structure of the data in spreadsheets, which otherwise could provide the means to automatically understand and extract knowledge from these files. In this paper, we propose a classification approach to discover the layout of tables in spreadsheets. Therefore, we focus on the cell level, considering a wide range of features not covered before by related work. We evaluated the performance of our classifiers on a large dataset covering three different corpora from various domains. Finally, our work includes a novel technique for detecting and repairing incorrectly classified cells in a post-processing step. The experimental results show that our approach deliver s very high accuracy bringing us a crucial step closer towards automatic table extraction.
dc.format.extent12 p.
dc.language.isoeng
dc.publisherSciTePress
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.lcshData mining
dc.subject.otherSpeadsheets
dc.subject.otherTabular
dc.subject.otherLayout
dc.subject.otherStructure
dc.subject.otherKnowledge discovery
dc.titleA machine learning approach for layout inference in spreadsheets
dc.typeConference report
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacMineria de dades
dc.contributor.groupUniversitat Politècnica de Catalunya. MPI - Modelització i Processament de la Informació
dc.identifier.doi10.5220/0006052200770088
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://dx.doi.org/10.5220/0006052200770088
dc.rights.accessOpen Access
local.identifier.drac19594455
dc.description.versionPostprint (published version)
local.citation.authorKoci, E.; Thiele, M.; Romero, O.; Lehner, W.
local.citation.contributorInternational Conference on Knowledge Discovery and Information Retrieval
local.citation.pubplacePorto
local.citation.publicationNameIC3K 2016: Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management: volume 1: KDIR
local.citation.startingPage77
local.citation.endingPage88


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