As open-source framework, Rasa NLU puts a special focus on full customizability. As result Rasa NLU provides you with several entity recognition components, which are able to target your custom requirements:

Custom SpaCy 3.0 models in Rasa

In this guide, we're going to bootstrap a spaCy 3.0 project and show you how you can integrate it with Rasa. We will train an entity detector and demonstrate how to use the new spaCy projects feature.…

Vincent Warmerdam

Exploring Semantic Map Embeddings / Part II

In this second part of our series on semantic maps, we show how to create them and see how they perform as featurizers for DIET.…

Johannes E. M. Mosig

Introducing Entity Roles and Groups

At a fundamental level, natural language understanding (NLU) does two things: it identifies the goal or meaning of the text and extracts key pieces of information…

Mady Mantha

Rasa NLU in Depth: Part 2 – Entity Recognition

Part 2 of our Rasa NLU in Depth series covers entity recognition. We will explain which components you should use for which type of entity and how to tackle common problems like fuzzy entities.…

Tobias Wochinger