一种用于数字词典错误检测的随机森林系统组合方法
A random forest system combination approach for error detection in digital dictionaries
摘要：
当印刷双语词典数字化时，无论是通过光学字符识别还是手动输入，都不可避免地会在创建的电子版本中引入错误。我们调查自动化的过程中检测错误的XML表示的数字化印刷字典使用的混合方法，结合基于规则的，基于特征的，和基于语言模型的方法。我们调查结合的方法，并表明，使用随机森林是一种很有前途的方法。我们发现，在隔离，无监督的方法竞争对手的监督方法的性能。随机森林通常需要训练数据，因此我们研究如何应用随机森林来组合单独的基本方法，这些方法本身是无监督的，而不需要大量的训练数据。实验经验表明，相对少量的数据就足够了，并且可以通过特定的选择标准进一步减少。
英文摘要：
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.
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