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Communications Mining User Guide

Last updated Nov 27, 2024

Understanding labels, general fields and metadata

Before designing your taxonomy, it’s important to understand what should be captured by labels, general fields, and metadata, to meet your objectives. There should be minimal overlaps as they all complement each other.

Labels:

  • Concepts, themes and intents
  • E.g. ‘Change of address request’, ‘Urgent’, ‘Status update request’, etc.
  • Should not be used to capture information that is present in the metadata

General Fields:

  • Structured data points extracted from the text
  • E.g. Policy numbers, trade IDs, URLs, dates, monetary quantities, etc.

Metadata:

  • Additional structured information associated with each message
  • Metadata properties can be user properties (defined and added pre-upload, e.g. NPS score), email properties (captured from emails, e.g. sender, recipients, domains, etc.), and thread properties (automatically derived by the platform for threaded data like emails and chats, e.g. # of messages in thread, thread duration, etc.)

Here are some of the key distinctions and similarities between labels and general fields. The two are typically used in combination for automation, but individually they serve different purposes:



What can the platform learn from during training?

The platform makes label predictions based on text of the message (for emails, this means the subject and body of the email), as well as some metadata properties. For general fields, it learns from the assigned span of text, and the context of the text surrounding that span.



Using labels, general fields and metadata together

Below is an example message that shows how labels, general fields and metadata are distinct, but complementary to one another. For this inbound request to be automated, each of them may be required for a specific purpose:



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