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(2000) Key: citeulike:12050165
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We present a method for automatically detecting errors in a manually marked corpus using anomaly detection. Anomaly detection is a method for determining which elements of a large data set do not conform to the whole. This method fits a probability distribution over the data and applies a statistical test to detect anomalous elements. In the corpus error detection problem, anomalous elements are typically marking errors. We present the results of applying this method to the tagged portion of the Penn Treebank corpus.
One-pass anomaly detection, in the sense of: given a full data set, "correct errors," which we hope are synonymous with anomalies.
An application of the ICML 2000 paper's method. Uses two models of normal for sequence data: naive Bayes, and Markov transducers. Their predictions were very similar.
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