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ANLP
2000

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Detecting Errors within a Corpus using Anomaly Detection

14 years 7 days ago

Detecting Errors within a Corpus using Anomaly Detection

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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.

Eleazar Eskin

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ANLP 2000 | Anomalous Elements | Anomaly Detection | Corpus Error Detection |

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Added	01 Nov 2010
Updated	01 Nov 2010
Type	Conference
Year	2000
Where	ANLP
Authors	Eleazar Eskin

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