Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

9

ANLP
2000

favoriteEmaildiscussreport

92views more ANLP 2000»

Detecting Errors within a Corpus using Anomaly Detection

13 years 5 months ago

Detecting Errors within a Corpus using Anomaly Detection

Download acl.ldc.upenn.edu

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

Real-time Traffic

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

claim paper

Related Content

» OnLine Error Detection of Annotated Corpus Using Modular Neural Networks

» Graphbased anomaly detection

» Active Learning for Anomaly and RareCategory Detection

» Detecting Anomalous and Unknown Intrusions Against Programs

» CorpusBased Syntactic Error Detection Using Syntactic Patterns

» Using Articulatory Representations to Detect Segmental Errors in Nonnative Pronunciation

» They Can Help Using Crowdsourcing to Improve the Evaluation of Grammatical Error Detection...

» Weakly Supervised Learning of a Classifier for Unusual Event Detection

» Combining Adaptive Filtering and IF Flows to Detect DDoS Attacks within a Router

Post Info
More Details (n/a)

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

Comments (0)