Difference between revisions of "Vulcan/MeetingNotes/Aug16 2013"
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: 1. Framework: Vulcan has a good evaluation interface setup. We will use this for starters.<br/> | : 1. Framework: Vulcan has a good evaluation interface setup. We will use this for starters.<br/> | ||
− | : 2. Data: Training/Test splits set up by Vulcan. | + | : 2. Data: Training/Test splits set up by Vulcan. |
<blockquote> | <blockquote> | ||
+ | Training data has a total of | ||
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+ | !Grade !! # Questions !! #Mult.Choice and Non-diag. | ||
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+ | |4th grade || 249 || 108 | ||
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+ | |8th grade || 476 || 125 | ||
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+ | | 12th grade || 446 || 160 | ||
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+ | | AP || 116 || 81 | ||
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+ | | All || 1287 || 474 | ||
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: 3. Method: Input sentences that correspond to each assertion. Score assertions using our system and submit to Vulcan's web interface.<br/> | : 3. Method: Input sentences that correspond to each assertion. Score assertions using our system and submit to Vulcan's web interface.<br/> |
Revision as of 18:08, 16 August 2013
Update
- System development (See detailed architecture and status)
- 1. Online inference components implemented.
- Proposition generator -- Extract tuples from input sentence and convert into a proposition.
- Evidence finder -- Tuple matching over Open IE Clueweb data.
- MLN Inference -- A wrapper around Tuffy's MLN inferencer.
- 2. Offline components -- axioms and rule generation -- NOT implemented.
- Experiments and Evaluation
- 1. Framework: Vulcan has a good evaluation interface setup. We will use this for starters.
- 2. Data: Training/Test splits set up by Vulcan.
Training data has a total of
Grade # Questions #Mult.Choice and Non-diag. 4th grade 249 108 8th grade 476 125 12th grade 446 160 AP 116 81 All 1287 474
- 3. Method: Input sentences that correspond to each assertion. Score assertions using our system and submit to Vulcan's web interface.
- Design questions.
- 1. Why not use MLN directly? Why use a backward chained inferencer (such as Jena) as an intermediate step?
- Looks like a separte backward-chained inferencer won't be necessary.
- Tuffy, an MLN implementation, does KBMC to scale MLN inference. Details [1]
- 2.
- Analysis
- 1. Selected 10 propositions that are single Open IE tuples as starting targets.
- 2. Wrote down steps involved in verifying these propositions.
Agenda
To Do (Copied over from previous week)
- System building
- 1. Implement "template matching" using the ClueWeb corpus.Pending
- URL for Open IE backend is available.
- For an assertion A, find sentences that have high overlap. Generate regex patterns for the proposition. Score sentences by how well they match the regex patterns.
- 2. Continue system building.
- Create a derivation scorer stub. This will be replaced with a MLN or a BLP scorer. Done.
- Test with iron nail example.
- 3. Jena API doesn't readily support multiple derivations.
- Ask Jena community to find out if this is possible. Done. Not possible.
- OWLIM as replacement. Done. Doesn't look promising. No response from community.
- 4. Try out Tuffy MLN implemenatation. Done.
- Use output of iron nail example
- If easy to use write wrappers around Tuffy to hook into our system.
- 5 Write evaluation code. Vulcan has a good interface set up.
- Check with Peter.
- 6. Create a system architecture page with a figure and overview of the main components.
Created a System status page instead.
- Created a figure. Added it to system design document.
- Need to create a wiki page for system architecture and overview.
- Experiments Pending
- 1. Run template matching approach as a baseline.
- 2. Run inference system as a baseline.