mailgun/talon

More granular message / signature parts classification

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#63 opened on Oct 9, 2015

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 (0 comments) (0 reactions) (0 assignees)Python (1,220 stars) (283 forks)batch import
enhancementhelp wanted

Description

The algo would be more accurate if the classification is more detailed. E.g. if it classifies message greeting there could be a sanity check saying that signature can't go right after a greeting.

Similarly if a closing phrase (e.g. "Kind Regards,", "Thanks,", etc) is detected in signature candidate lines we could say for sure that it should be the first signature line.

Another example - disclaimers. Some messages have them and that totally breaks signature detection logic.

Contributor guide

Tech stack
python
Domain
backend
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
mostly clear
Prerequisites
Pythonunderstanding of email structure
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
30
Research direction
Investigate the current signature detection algorithm in the repository. The issue suggests adding more granular classification of message parts (e.g., greeting, closing, disclaimer) to improve accuracy. Look for the existing classification logic, likely in a module like 'signature.py', and propose a more detailed categorization. Consider edge cases like disclaimers and ensure the new classification integrates with existing sanity checks.