Enhancing document structure analysis using visual analytics

Lade...
Vorschaubild
Dateien
keim.pdf
keim.pdfGröße: 626.8 KBDownloads: 588
Datum
2010
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
Proceedings of the 2010 ACM Symposium on Applied Computing - SAC '10. New York, New York, USA: ACM Press, 2010, pp. 8-12. ISBN 978-1-60558-639-7. Available under: doi: 10.1145/1774088.1774091
Zusammenfassung

During the last decade national archives, libraries, museums and companies started to make their records, books and files electronically available. In order to allow efficient access of this information, the content of the documents must be stored in database and information retrieval systems. State-of-the-art indexing techniques mostly rely on the information explicitly available in the text portions of documents. Documents usually contain a significant amount of implicit information such as their logical structure which is not directly accessible (unless the documents are available as well-structured XML-files) and is therefore not used in the search process. In this paper, a new approach for analyzing the logical structure of text documents is presented. The problem of state-of-the-art methods is that they have been developed for a particular type of documents and can only handle documents of that type. In most cases, adaptation and re-training for a different document type is not possible. Our proposed method allows an efficient and effective adaptation of the structure analysis process by combining state-of-the-art machine learning with novel interactive visualization techniques, allowing a quick adaptation of the structure analysis process to unknown document classes and new tasks without requiring a predefined training set.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
The 2010 ACM Symposium on Applied Computing - SAC '10, 22. März 2010 - 26. März 2010, Sierre, Switzerland
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690STOFFEL, Andreas, David SPRETKE, Henrik KINNEMANN, Daniel A. KEIM, 2010. Enhancing document structure analysis using visual analytics. The 2010 ACM Symposium on Applied Computing - SAC '10. Sierre, Switzerland, 22. März 2010 - 26. März 2010. In: Proceedings of the 2010 ACM Symposium on Applied Computing - SAC '10. New York, New York, USA: ACM Press, 2010, pp. 8-12. ISBN 978-1-60558-639-7. Available under: doi: 10.1145/1774088.1774091
BibTex
@inproceedings{Stoffel2010Enhan-12727,
  year={2010},
  doi={10.1145/1774088.1774091},
  title={Enhancing document structure analysis using visual analytics},
  isbn={978-1-60558-639-7},
  publisher={ACM Press},
  address={New York, New York, USA},
  booktitle={Proceedings of the 2010 ACM Symposium on Applied Computing - SAC '10},
  pages={8--12},
  author={Stoffel, Andreas and Spretke, David and Kinnemann, Henrik and Keim, Daniel A.}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/12727">
    <dc:contributor>Kinnemann, Henrik</dc:contributor>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/12727/2/keim.pdf"/>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dcterms:title>Enhancing document structure analysis using visual analytics</dcterms:title>
    <dc:language>eng</dc:language>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-09-21T07:55:47Z</dcterms:available>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/12727"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/12727/2/keim.pdf"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:abstract xml:lang="eng">During the last decade national archives, libraries, museums and companies started to make their records, books and files electronically available. In order to allow efficient access of this information, the content of the documents must be stored in database and information retrieval systems. State-of-the-art indexing techniques mostly rely on the information explicitly available in the text portions of documents. Documents usually contain a significant amount of implicit information such as their logical structure which is not directly accessible (unless the documents are available as well-structured XML-files) and is therefore not used in the search process. In this paper, a new approach for analyzing the logical structure of text documents is presented. The problem of state-of-the-art methods is that they have been developed for a particular type of documents and can only handle documents of that type. In most cases, adaptation and re-training for a different document type is not possible. Our proposed method allows an efficient and effective adaptation of the structure analysis process by combining state-of-the-art machine learning with novel interactive visualization techniques, allowing a quick adaptation of the structure analysis process to unknown document classes and new tasks without requiring a predefined training set.</dcterms:abstract>
    <dc:contributor>Spretke, David</dc:contributor>
    <dc:rights>terms-of-use</dc:rights>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-09-21T07:55:47Z</dc:date>
    <dc:creator>Stoffel, Andreas</dc:creator>
    <dc:creator>Spretke, David</dc:creator>
    <dc:contributor>Stoffel, Andreas</dc:contributor>
    <dcterms:bibliographicCitation>First publ. in: SAC '10 Proceedings of the 2010 ACM Symposium on Applied Computing. - 2010. - S. 8-12. - ISBN 978-1-60558-639-7</dcterms:bibliographicCitation>
    <dc:creator>Kinnemann, Henrik</dc:creator>
    <dcterms:issued>2010</dcterms:issued>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen