Comments (2)
from potato.
Moved "3rd panel listing all sentences. User can "label" them by selecting positive examples, rules can be trained based on those examples, user can see which other sentences these rules would trigger, then they can also label those sentences to mark that they are positive or negative examples (thereby increasing the amount of labeled data, one sentence at a time), and can choose to accept rules, reject rules, or "train rules" (i.e. refine them), thereby refining the rule system as they go along" into a separate issue, as it is a major task.
from potato.
Related Issues (20)
- UI enhancement HOT 3
- Implement caching of the graphs for evaluation
- New scikit-criteria 0.5 HOT 2
- Instead of pickle, store Dataframes in TSV with penman formatted graphs
- The frontend should have an inference mode and better instructions
- pattern matching bug (caused by self-loops?) HOT 1
- sentence split in multiple 4lang graphs not merged HOT 2
- error in training when using all classes of brise data HOT 2
- "suggest rules" should be able to run only on false negatives (i.e. only on what isn't covered already)
- very good keyword not found by "suggest rules"
- matching rule not highlighted in graphs when using regexes?
- Resolving pip backtracking takes too long
- Sometimes AMR to networkx conversion merges an edge into a nodename
- how hard to use this with a very large corpus? HOT 1
- proper support for multi-label datasets
- Can this help with mining names (of people or concepts or groups or ...)?
- 'PotatoGraph' object has no attribute 'nodes' HOT 2
- Building rules for homophobia detection - issues
- Tests for Matchers HOT 2
- KeyError: "Constituency parser not trained with tag 'GW'"
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from potato.