Text Information Extraction
author:
Kamal Nigam,
Google
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| Slides | |
| 0:00 | Machine Learning for Information Extraction: An Overview |
| 0:13 | Example: A Problem |
| 1:54 | Example: A Solution |
| 2:31 | Job Openings: Category = Food Services Keyword = Baker Location = Continental U.S. |
| 2:50 | Extracting Job Openings from the Web |
| 3:41 | Potential Enabler of Faceted Search |
| 4:28 | Lots of Structured Information in Text |
| 5:01 | IE from Research Papers |
| 6:00 | What is Information Extraction? |
| 8:28 | What is Information Extraction?01 |
| 9:05 | What is Information Extraction?02 |
| 9:58 | What is Information Extraction?03 |
| 11:01 | IE History |
| 11:32 | IE Posed as a Machine Learning Task |
| 13:43 | Good Features for Information Extraction |
| 15:15 | Good Features for Information Extraction01 |
| 15:50 | Landscape of ML Techniques for IE: |
| 16:53 | Sliding Windows & Boundary Detection |
| 17:00 | Information Extraction by Sliding Windows |
| 17:23 | Information Extraction by Sliding Window01 |
| 17:35 | Information Extraction by Sliding Window02 |
| 17:36 | Information Extraction by Sliding Window03 |
| 17:42 | Information Extraction with Sliding Windows |
| 21:03 | IE by Boundary Detection |
| 21:19 | IE by Boundary Detection01 |
| 21:24 | IE by Boundary Detection02 |
| 21:25 | IE by Boundary Detection03 |
| 21:35 | IE by Boundary Detection04 |
| 21:59 | BWI: Learning to detect boundaries |
| 23:12 | Problems with Sliding Windows and Boundary Finders |
| 24:22 | Finite State Machines |
| 24:35 | Hidden Markov Models |
| 32:15 | Generative Extraction with HMMs |
| 36:00 | HMM Example: “Nymble” |
| 55:09 | Sample IE Applications of CRFs |
| 55:53 | Examples of Recent CRF Research |
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Great lecture.
Unfortunately the slides available for download are not the same as the ones used by Mr. Nigam. Otherwise I find the lecture very informing and well presented.
Thank You.
Very informative. Thank you.