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. | ||
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{| class="wikitable" | {| class="wikitable" | ||
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− | !Training !! All !! MC-Only !! Non-Diagrams-Only !! MC-Non-Diagrams-Only | + | |!Training !! All !! MC-Only !! Non-Diagrams-Only !! MC-Non-Diagrams-Only |
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|4th grade !! 35.16% !! 52.55% !! 49.58% !! 55.09% | |4th grade !! 35.16% !! 52.55% !! 49.58% !! 55.09% | ||
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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 17:51, 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 !! All !! MC-Only !! Non-Diagrams-Only !! MC-Non-Diagrams-Only |
4th grade !! 35.16% !! 52.55% !! 49.58% !! 55.09% |
8th grade !! 23.01% !! 43.46% !! 45.29% !! 55.07% |
12th grade !! 17.06% !! 31.29% !! 14.11% !! 25.83% |
AP !! 22.55% !! 41.92% !! 30.58% !! 45.68% |
- 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.