Robust Textual Inference Using Diverse Knowledge Sources
author:
Rajat Raina,
Stanford University
Categories
Top: Computer Science: Machine Learning: Human Language TechnologyTop: Computer Science: Information Extraction
Top: Computer Science: Text Mining
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| Slides | |
| 0:00 | RTE @ Stanford |
| 0:24 | Our approach |
| 1:18 | Outline of this talk |
| 2:11 | Sentence processing |
| 3:38 | Named Entity Recognizer |
| 4:38 | Parse tree post-processing |
| 5:27 | Parse tree Dependencies |
| 6:52 | Representations |
| 8:07 | Annotations |
| 8:56 | More annotations |
| 10:00 | Event nouns |
| 11:19 | Outline of this talk |
| 11:30 | Graph Matching Approach |
| 12:17 | Graph Matching: Idea |
| 12:54 | Graph Matching: Costs |
| 14:13 | Graph Matching: Costs |
| 14:50 | Digression: Phrase similarity |
| 16:03 | Graph Matching: Costs |
| 16:27 | Graph Matching: Example |
| 17:08 | Outline of this talk |
| 17:20 | Abductive inference |
| 19:09 | Abductive assumptions |
| 20:56 | Abductive theorem proving |
| 21:10 | Abductive theorem proving |
| 21:46 | Some interesting features |
| 22:22 | Some interesting features |
| 22:30 | Outline of this talk |
| 22:32 | Results |
| 22:57 | Results |
| 23:19 | Results by task |
| 23:29 | Partial coverage results |
| 24:34 | Some interesting issues |
| 25:05 | Future directions |
| 25:20 | Thanks! |
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