Elie: A two-level boundary classification approach to adaptive information extraction
author:
Aidan Finn,
Institute of Technology Sligo,
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| Slides | |
| 0:01 | Information Extraction by Multi-Level Boundary Classification |
| 0:21 | Information Extraction |
| 0:57 | Information Extraction |
| 1:23 | Adaptive Information extraction |
| 1:47 | Overview |
| 2:23 | IE as classification |
| 3:27 | IE as classification |
| 3:50 | Features |
| 4:34 | Encoding example |
| 5:15 | Performance |
| 5:45 | Precision |
| 6:15 | Recal |
| 6:24 | Improving performance |
| 7:14 | Learning with ELIE |
| 7:37 | L1 and L2 learning |
| 8:14 | L1 and L2 |
| 10:00 | Learning |
| 10:13 | Extracting |
| 10:15 | Experiments L2 |
| 10:54 | Precision |
| 11:36 | Recal |
| 12:00 | F-measure |
| 12:30 | Precision |
| 12:39 | Recall |
| 12:42 | F-measure |
| 12:45 | Pascal challenge task |
| 13:43 | Pascal challenge results |
| 15:14 | Reasons ELIE performs poorly |
| 16:14 | Performance loss with Imbalanced Data |
| 17:17 | Imbalanced Data |
| 18:11 | Imbalance in IE datasets |
| 19:01 | Improvements/Future work |
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