Handling Email Signatures in Unstructured Inbound Emails (Email Channel – v24.2.2)

General
1

Hi All,

Greetings!

I am currently working on a scenario where entities need to be extracted from inbound emails using the Email Channel in version 24.2.2.

The challenge is that the incoming emails are completely unstructured, with no predefined or consistent format. I initially attempted to address this by training the email parser (pxEmailParser) using a set of sample emails. While this worked initially, an issue has emerged when new email formats are introduced and trained. Specifically, it has been observed that some of the previously trained email formats— which were functioning correctly—stop working after new training sessions.

This appears to be caused by incorrect classification of the subject, body, and signature by the email parser. Given that regular retraining is not feasible, waiting for the model to gradually become robust is not a practical option.

As an alternative, I am considering removing the email parser entirely. However, this introduces another challenge: the email signature. In several cases, information present in the signature section leads to incorrect identification of both entities and intent.

Therefore, I would like to seek guidance from the community on the following:

  • Is there a reliable way to filter out or isolate only the signature portion from inbound emails?

  • Can this be achieved even when the signature appears in trailing email content (for example, in forwarded or replied messages)?

Any suggestions, best practices, or approaches to handle this scenario would be greatly appreciated.

Thanks & regards,
Viswanath

2

Email signature isolation is a separate from entity extraction and if the parser is already unstable, relying on re-training alone is usually not the best long-term option. I’d recommend to first split the email into body vs. quoted reply/forward vs. signature and then run entity extraction on the cleaned body text.

I think there is no guaranteed perfect way to isolate a signature in every case, especially when forwarded/replied emails are mixed. Nested email content makes the problem harder because the ‘signature’ may appear multiple times.

  • If you can avoid it, do not depend on signature text for entity extraction. Treat it as a noise.
  • Pre-clean the inbound email text before extraction using a preprocessing step to strip quoted replies and likely signature blocks
  • Use stable business rules / heuristics for signature boundaries rather than continuously retraining on every new sample.
  • If the extraction quality still isn’t good enough, consider moving away from parser-driven extraction and toward a more controlled extraction pipeline

for forwarded and replied emails, detect the start of quoted history first and cut off content after common reply markers.