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When digitizing a print bilingual dictionary, whether via optical characterrecognition or manual entry, it is inevitable that errors are introduced intothe electronic version that is created. We investigate automating the processof detecting errors in an XML representation of a digitized print dictionaryusing a hybrid approach that combines rule-based, feature-based, and languagemodel-based methods. We investigate combining methods and show that usingrandom forests is a promising approach. We find that in isolation, unsupervisedmethods rival the performance of supervised methods. Random forests typicallyrequire training data so we investigate how we can apply random forests tocombine individual base methods that are themselves unsupervised withoutrequiring large amounts of training data. Experiments reveal empirically that arelatively small amount of data is sufficient and can potentially be furtherreduced through specific selection criteria.

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