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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yang Liu Shriberg, E. |
| Copyright Year | 2007 |
| Description | Author affiliation: Dept. of Comput. Sci., Texas Univ., Richardson, TX, USA (Yang Liu) |
| Abstract | In recent NIST evaluations on sentence boundary detection, a single error metric was used to describe performance. Additional metrics, however, are available for such tasks, in which a word stream is partitioned into subunits. This paper compares alternative evaluation metrics - including the NIST error rate, classification error rate per word boundary, precision and recall, ROC curves, DET curves, precision-recall curves, and area under the curves - and discusses advantages and disadvantages of each. Unlike many studies in machine learning, we use real data for a real task. We find benefit from using curves in addition to a single metric. Furthermore, we find that data skew has an impact on metrics, and that differences among different system outputs are more visible in precision-recall curves. Results are expected to help us better understand evaluation metrics that should be generalizable to similar language processing tasks. |
| File Size | 4789254 |
| File Format | |
| ISBN | 1424407273 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2007.367194 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-04-15 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | NIST Error analysis Speech analysis Computer science Machine learning Speech recognition System performance Humans Computer errors Event detection ROC curve sentence boundary detection precision recall |
| Content Type | Text |
| Resource Type | Article |
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