Textual Entailment
author:
Ido Dagan,
Bar Ilan University
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| Slides | |
| 0:00 | - Announcement |
| 2:33 | PASCAL Pump Priming Project on Textual Entailment |
| 3:35 | Project Outline – Lexical Entailment |
| 5:26 | Bar Ilan |
| 5:31 | Applied Textual Entailment: Abstracting Variability Inference |
| 6:44 | Foundations and Infrastructure |
| 7:20 | Probabilistic Textual Entailment |
| 9:43 | Lexical Alignment (1st Model) |
| 10:34 | Results on RTE 1 dataset |
| 10:55 | Contextual entailment (2nd model) – as a text categorization task |
| 12:52 | Document Representation for Entailment |
| 13:25 | First intent: Theme Topic Mixture Model (TTMM) |
| 13:47 | Second intent: Neural Network for Text Representation (NNTR) |
| 14:58 | Architecture and Results |
| 15:48 | CNTS Subtitling Application for Evaluation of Entailment |
| 16:15 | Subtitling project: MUSA |
| 16:38 | Entailment in subtitle generation |
| 17:21 | Example |
| 18:10 | Rate compression possibilities |
| 19:09 | Evaluation |
| 21:09 | Results |
| 22:42 | XRCE Textual Entailment for IR |
| 22:55 | Textual Entailment for Information Retrieval |
| 23:06 | The language model approach to IR |
| 23:43 | Textual entailment as translation |
| 23:55 | Information Retrieval results |
| 24:25 | Meetings |
| 24:39 | Follow up activities: XRCE |
| 25:25 | Follow up activities: CNTS |
| 25:54 | Follow up activities: BIU |
| 27:17 | Takeout: New Research Problems |
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