A label taxonomy is a hierarchical structure of concepts that you want to capture from your communications data, such as emails, chats, or calls. Each label represents a specific concept that serves a business purpose and is aligned to your objectives. A label taxonomy can have multiple levels of hierarchy, where each child label is a subset of its parent label. For example, a parent label could be “Product Feedback” and a child label could be “Product Feature Request” or “Product Bug Report”. A label taxonomy is used to train a machine learning model that can automatically classify your communications data according to the labels you defined1.
One of the best practices for designing a label taxonomy is to ensure that each label is clearly identifiable from the text of the individual verbatim (not thread) to which it will be applied. A verbatim is a single unit of communication, such as an email message, a chat message, or a call transcript segment. A thread is a collection of related verbatims, such as an email conversation, a chat session, or a call recording. When you train your model, you will apply labels to verbatims, not threads, so it is important that each label can be recognized from the verbatim text alone, without relying on the context of the thread. This will help the model to learn the patterns and features of each label and to generalize to new data. It will also help you to maintain consistency and accuracy when labelling your data2.
References: 1: Communications Mining - Taxonomies 2: Communications Mining - Label hierarchy and best practice
