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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9 comments
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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.
Well done detailed lecture on how to do things, unlike many others were presenters throw formula's on the screen and leave it as an exercise for the reader to determine how to put it into practice.