A random forest system combination approach for error detection in digital dictionaries

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Date
2012-04-23Author
Bloodgood, Michael
Ye, Peng
Rodrigues, Paul
Zajic, David
Doermann, David
Citation
Michael Bloodgood, Peng Ye, Paul Rodrigues, David Zajic, and David Doermann. A random forest system combination approach for error detection in digital dictionaries. In Proceedings of the EACL Workshop on Innovative Hybrid Approaches to the Processing of Textual Data (Hybrid2012), pages 78-86. Association for Computational Linguistics, 2012.
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Show full item recordAbstract
When digitizing a print bilingual dictionary,
whether via optical character recognition or
manual entry, it is inevitable that errors are introduced into the electronic version that is created. We investigate automating the process of detecting errors in an XML representation of a digitized print dictionary using a hybrid approach that combines rule-based, feature-based, and language model-based methods. We investigate combining methods and show that using random
forests is a promising approach. We find
that in isolation, unsupervised methods rival the performance of supervised methods.
Random forests typically require training
data so we investigate how we can apply
random forests to combine individual base
methods that are themselves unsupervised
without requiring large amounts of training
data. Experiments reveal empirically that
a relatively small amount of data is sufficient and can potentially be further reduced through specific selection criteria.