Difference between revisions of "102113Notes"
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We discussed the three major user groups. | 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. | 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
