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SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Machine Learning
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Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
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ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
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CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
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WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
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ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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SEALTS '03 Proceedings of the HLT-NAACL 2003 workshop on Software engineering and architecture of language technology systems - Volume 8
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This paper describes the information properties of museum specimen labels and machine learning tools to automatically extract Darwin Core (DwC) and other metadata from these labels processed through Optical Character Recognition (OCR). The DwC is a metadata profile describing the core set of access points for search and retrieval of natural history collections and observation databases. Using the HERBIS Learning System (HLS) we extract 74 independent elements from these labels. The automated text extraction tools are provided as a web service so that users can reference digital images of specimens and receive back an extended Darwin Core XML representation of the content of the label. This automated extraction task is made more difficult by the high variability of museum label formats, OCR errors and the open class nature of some elements. In this paper we introduce our overall system architecture, and variability robust solutions including, the application of Hidden Markov and Naïve Bayes machine learning models, data cleaning, use of field element identifiers, and specialist learning models. The techniques developed here could be adapted to any metadata extraction situation with noisy text and weakly ordered elements.