Difference between revisions of "102113Notes"
From Knowitall
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− | == | + | == Log == |
− | + | '''October 23 2013 Meeting ''' | |
− | + | We discussed the three major user groups. | |
+ | # NLP Researchers - customize feature generation, argument identification, noise reduction, etc. | ||
+ | # Power Users - use custom knowledge bases and relations | ||
+ | # Novice User - uses out of the box system | ||
− | + | The better the system is for the Novice user the more successful this project will be. | |
− | + | ||
+ | Some Important Requirements we discussed: | ||
+ | # NEL-capability for Argument Identification | ||
+ | # Negative Examples | ||
+ | # Noise Reduction Component | ||
+ | |||
+ | |||
+ | <br /> | ||
+ | We decided to follow Mitchell's suggestion and use Stanford CoreNLP's native data structures when appropriate | ||
+ | |||
+ | |||
+ | Milestones/Goals | ||
+ | # Generate Distant Supervision data from preprocessed corpus. (10/29) | ||
+ | # Run Preprocessing code on new textual data (1/1) | ||
+ | # Provide Interface for NEL with external KB in Argument Identification (11/6) | ||
+ | # Run Original Multir Algorithm with new implementation (11/8) | ||
+ | # Run Multir with NEL-Argument Identification | ||
+ | # Extend Preprocessing interface to allow for custom preprocessing schemes | ||
+ | # Establish working web-demo | ||
+ | # Add noise-reduction component and negative example components |
Latest revision as of 00:30, 24 October 2013
Log
October 23 2013 Meeting
We discussed the three major user groups.
- NLP Researchers - customize feature generation, argument identification, noise reduction, etc.
- Power Users - use custom knowledge bases and relations
- Novice User - uses out of the box system
The better the system is for the Novice user the more successful this project will be.
Some Important Requirements we discussed:
- NEL-capability for Argument Identification
- Negative Examples
- Noise Reduction Component
We decided to follow Mitchell's suggestion and use Stanford CoreNLP's native data structures when appropriate
Milestones/Goals
- Generate Distant Supervision data from preprocessed corpus. (10/29)
- Run Preprocessing code on new textual data (1/1)
- Provide Interface for NEL with external KB in Argument Identification (11/6)
- Run Original Multir Algorithm with new implementation (11/8)
- Run Multir with NEL-Argument Identification
- Extend Preprocessing interface to allow for custom preprocessing schemes
- Establish working web-demo
- Add noise-reduction component and negative example components