Functional Semantic Categories for Art History Text - Human Labeling and Preliminary Machine Learning

Rebecca J. Passonneau, Tae Yano, Tom Lippincott, Judith Klavans

2008

Abstract

The CLiMB project investigates semi-automatic methods to extract descriptive metadata from texts for indexing digital image collections. We developed a set of functional semantic categories to classify text extracts that describe images. Each semantic category names a functional relation between an image depicting a work of art historical significance, and expository text associated with the image. This includes description of the image, discussion of the historical context in which the work was created, and so on. We present interannotator agreement results on human classification of text extracts, and accuracy results from initial machine learning experiments. In our pilot studies, human agreement varied widely, depending the labeler’s expertise, the image-text pair under consideration, the number of labels that could be assigned to one text, and the type of training, if any, we gave labelers. Initial machine learning results indicate the three most relevant categories are machine learnable. Based on our pilot work, we implemented a labeling interface that we are currently using to collect a large dataset of text that will be used in training and testing machine classifiers.

References

  1. R. Artstein and M. Poesio. Kappa3 = alpha (or beta). Technical Report NLE Technote 2005- 01, University of Essex, Essex, 2005.
  2. M. Baca. Practical Issues in Applying Metadata Schemas and Controlled Vocabularies to Cultural Heritage Information. The Haworth Press, Inc., 2003. Available through Library Literature, last accessed July 25, 2006.
  3. H. Chen. An analysis of image queries in the field of art history. Journal of the American Society for Information Science and Technology, pages 260-273, 2001.
  4. A. Giral and A. Taylor. Indexing overlap and consistency between the Avery Index to Architectural Periodicals and the Architectural Periodicals Index. Library Resources and Technical Services 37(1):19-44, 1993.
  5. J. Klavans. Using computational linguistic techniques and thesauri for enhancing metadata records in image search: The CLiMB project. Article in preparation.
  6. K. Krippendorff. Content Analysis: An Introduction to Its Methodology. Sage Publications, Beverly Hills, CA, 1980.
  7. S. S. Layne. Some issues in the indexing of images. Journal of the American Society for Information Science, pages 583-8, 1994.
  8. K. Markey. Interindexer consistency tests: a literature review and report of a test of consistency in indexing visual materials. Library and Information Science Research, pages 155- 177, 1984.
  9. R. Passonneau. Measuring agreement on set-valued items (MASI) for semantic and pragmatic annotation. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC), 2006.
  10. R. Passonneau, N. Habash and O. Rambow. Inter-annotatator agreement on a multilingual semantic annotation task. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC), 2006.
  11. R. J. Passonneau, D. Elson, R. Blitz, and J. Klavans. CLiMB Toolkit: A case study of iterative evaluation in a multidis ciplinary project. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC), 2006.
  12. D. Riedsma and J. Carletta. Reliability measurement: there's no safe limit To appear in Computational Linguistics.
  13. I. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann: San Fransisco, 2000.
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Paper Citation


in Harvard Style

J. Passonneau R., Yano T., Lippincott T. and Klavans J. (2008). Functional Semantic Categories for Art History Text - Human Labeling and Preliminary Machine Learning . In Metadata Mining for Image Understanding - Volume 1: MMIU, (VISIGRAPP 2008) ISBN 978-989-8111-24-1, pages 13-22. DOI: 10.5220/0002340300130022


in Bibtex Style

@conference{mmiu08,
author={Rebecca J. Passonneau and Tae Yano and Tom Lippincott and Judith Klavans},
title={Functional Semantic Categories for Art History Text - Human Labeling and Preliminary Machine Learning},
booktitle={Metadata Mining for Image Understanding - Volume 1: MMIU, (VISIGRAPP 2008)},
year={2008},
pages={13-22},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002340300130022},
isbn={978-989-8111-24-1},
}


in EndNote Style

TY - CONF
JO - Metadata Mining for Image Understanding - Volume 1: MMIU, (VISIGRAPP 2008)
TI - Functional Semantic Categories for Art History Text - Human Labeling and Preliminary Machine Learning
SN - 978-989-8111-24-1
AU - J. Passonneau R.
AU - Yano T.
AU - Lippincott T.
AU - Klavans J.
PY - 2008
SP - 13
EP - 22
DO - 10.5220/0002340300130022