This section provides Conceptual Overviews of some of the functionalities in the Teneo Platform. The pages are meant to provide the reader with a high-level overview and better understanding of how the Teneo components work in order to make the most of the features.
Currently, this section includes the below listed pages:
- Intent Classification describes the functionalities around creating and improving a machine learning model in Teneo including the input processing performed, the generation of annotations and the cross-validation process available allowing to estimate the performance of the solution's machine learning model
- From Request to Response goes over the process happening from when a user input is sent to Teneo Engine to an output answer is returned
- Log Data Handling provides an introduction to log handling in Teneo
- Session Data Model details how the session data model is structured, the type of data that is generated and used and is indented to help understand how to perform Teneo Query Language queries
- NLU Generation illustrates how the automatic NLU Generation for TLML Syntax Matches works, gives insights on requirements for the positive User Intents as well as information related to the selected Language Objects and Entities
- Annotating Inputs answers what annotations are and where they come from alongside providing an introduction on how to create custom annotations in projects using relevant Engine Scripting API methods