mirror of
https://github.com/NLPatVCU/medaCy.git
synced 2026-04-03 00:09:00 -06:00
[FEATURE REQUEST] Functionality for analyzing the differences between two Annotation objects. #147
Labels
No labels
High Priority
High Priority
High Priority
High Priority
Requires Code Refactoring
Requires Code Refactoring
Requires Code Refactoring
Requires Code Refactoring
Requires Code Refactoring
Requires Code Refactoring
Requires Code Refactoring
Requires Code Refactoring
Requires Code Refactoring
Requires Code Refactoring
Requires ML Background
Requires ML Background
Requires ML Background
bug
bug
bug
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
enhancement
good first issue
good first issue
good first issue
good first issue
good first issue
good first issue
wontfix
No milestone
No project
No assignees
1 participant
Notifications
Due date
No due date set.
Dependencies
No dependencies set.
Reference: github/medaCy#147
Loading…
Add table
Add a link
Reference in a new issue
No description provided.
Delete branch "%!s()"
Deleting a branch is permanent. Although the deleted branch may continue to exist for a short time before it actually gets removed, it CANNOT be undone in most cases. Continue?
Originally created by @AndriyMulyar on 12/28/2018
What problem does your feature solve?
A method to do analysis of annotations (namely for the application of looking at differences between gold and predicted annotations).
Describe the solution you'd like
The Annotation class should be given some static methods like
Annotation.diff(ann_object_1, ann_object_2)will output the difference between to annotation objects. Maybe some parameter for leniency to deal with fuzzy annotation matching.Interface sklearn to compute various evaluation metrics between two annotation files (assuming one is gold and one is predicted).
Additional context
This would be very useful for result analysis and guiding the building of pipelines.