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Computational linguistics for metadata building (CLiMB): using text mining for the automatic identification, categorization, and disambiguation of subject terms for image metadata

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Abstract

In this paper, we present a system using computational linguistic techniques to extract metadata for image access. We discuss the implementation, functionality and evaluation of an image catalogers’ toolkit, developed in the Computational Linguistics for Metadata Building (CLiMB) research project. We have tested components of the system, including phrase finding for the art and architecture domain, functional semantic labeling using machine learning, and disambiguation of terms in domain-specific text vis a vis a rich thesaurus of subject terms, geographic and artist names. We present specific results on disambiguation techniques and on the nature of the ambiguity problem given the thesaurus, resources, and domain-specific text resource, with a comparison of domain-general resources and text. Our primary user group for evaluation has been the cataloger expert with specific expertise in the fields of painting, sculpture, and vernacular and landscape architecture.

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Notes

  1. Some examples include OntoImage’2006—First International “Language Resources for Content-Based Image Retrieval” Workshop, held in conjunction with the Language Resources and Evaluation Conference (LREC) 2006, http://www.lrec-conf.org/lrec2006; OntoImage’2008—Second 2nd International “Language Resources for Content-Based Image Retrieval” Workshop, held in conjunction with LREC’2008, http://www.dfki.de/∼declerck/ontoimage.html; workshops on computational linguistics for image access held at the Visual Resources Association annual meetings, 2006, 2007, 2008, http://www.vraweb.org.

  2. http://vraweb.org/ccoweb/cco/parttwo_chapter6.html.

  3. One such project, T 3 : Text, Tagging and Trust to Improve Image Access for Museums and Libraries, has just been funded from the Institute for Museum and Library Science, imls.gov.

  4. Some metadata standards mentioned in Baca 2003 were: Categories for the Description of Works of Art (CDWA) from the Getty Research Institute and Cataloging Cultural Objects (CCO) from the Visual Resources Association.

  5. Notable controlled vocabularies noted in Baca 2003 were: Library of Congress Subject Headings; Library of Congress Name Authority File; the Getty Vocabularies; Thesaurus for Graphic Materials I and II.

  6. http://www.vernaculararchitectureforum.org/.

  7. http://www.sah.org/.

  8. http://www.lair.umd.edu/.

  9. http://www.artstor.org.

  10. Both the tagger and parser are available at: http://nlp.stanford.edu/software.

  11. Lucene is a search engine library: http://lucene.apache.org.

  12. Getty resources can be accessed at: http://getty.edu/research/conducting_research/vocabularies/aat.

  13. According to the documentation on the TGN, natural order refers to searching on the most common order of a name, e.g. Al-Hoceima, whereas inverted order would be Hoceima, Al-.

  14. Steve: The Museum Social Tagging Project. http://www.steve.museum.

  15. Luis von Ahn: The ESP Game at Games with a Purpose (GWAP).

    http://www.gwap.com/gwap/gamesPreview/espgame/.

  16. Jennifer Golbeck: FilmTrust. http://www.mindswap.org.

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Acknowledgements

We acknowledge the Program Office for Scholarly Communications of the Andrew W. Mellon Foundation, especially Don Waters and Suzanne Lodato; Dr. Murtha Baca, director of the Getty Vocabulary Program and Digital Resource Management, Getty Research Institute for providing us with research access to resources; cataloging and domain expert Angela Giral; collections partners, including Jeff Cohen, Bryn Mawr College and University of Pennsylvania for the vernacular architecture collection; Jack Sullivan, University of Maryland for landscape architecture; the Senate Museum and Library; and ARTStor. Finally, Joan Beaudoin (Drexel), Laura Jaeneman (Drexel), and Brooke Rosenblatt (the Phillips Gallery) helped with annotation, collections and user studies.

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Authors and Affiliations

  1. iSchool, University of Maryland, College Park, MD, USA

    Judith L. Klavans, Carolyn Sheffield, Jimmy Lin, Tandeep Sidhu & Dagobert Soergel

  2. Computational Linguistics and Information Processing Laboratory (CLIP), University of Maryland, College Park, MD, USA

    Judith L. Klavans & Jimmy Lin

  3. University of Maryland Institute for Advanced Computer Science (UMIACS), University of Maryland, College Park, MD, USA

    Judith L. Klavans & Jimmy Lin

  4. College of Information Science and Technology, Drexel University, Philadelphia, PA, USA

    Eileen Abels

  5. Center for Computational Learning Systems, Columbia University, New York, NY, USA

    Rebecca Passonneau

Authors
  1. Judith L. Klavans
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  2. Carolyn Sheffield
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  3. Eileen Abels
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  4. Jimmy Lin
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  5. Rebecca Passonneau
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  6. Tandeep Sidhu
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  7. Dagobert Soergel
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Corresponding author

Correspondence to Carolyn Sheffield.

Additional information

This project, funded by the Andrew W. Mellon Foundation, was initiated at the Center for Research on Information Access at Columbia University and is currently based at the University of Maryland.

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Klavans, J.L., Sheffield, C., Abels, E. et al. Computational linguistics for metadata building (CLiMB): using text mining for the automatic identification, categorization, and disambiguation of subject terms for image metadata. Multimed Tools Appl 42, 115–138 (2009). https://doi.org/10.1007/s11042-008-0253-9

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