Entity Extraction

These docs are for version 1.x of Rasa Open Source.

User Guide

NLU

Core

Conversation Design

API Reference

Migrate from (beta)

Reference

Entity Extraction

Entity extraction involves parsing user messages for required pieces of information. Rasa Open Source provides entity extractors for custom entities as well as pre-trained ones like dates and locations. Here is a summary of the available extractors and what they are used for:

Component Requires Model Notes
CRFEntityExtractor sklearn-crfsuite conditional random field good for training custom entities
SpacyEntityExtractor spaCy averaged perceptron provides pre-trained entities
DucklingHTTPExtractor running duckling context-free grammar provides pre-trained entities
MitieEntityExtractor MITIE structured SVM good for training custom entities
EntitySynonymMapper existing entities N/A maps known synonyms
DIETClassifier conditional random field
on top of a transformer		 good for training custom entities

The “entity” Object

After parsing, an entity is returned as a dictionary. There are two fields that show information about how the pipeline impacted the entities returned: the extractor field of an entity tells you which entity extractor found this particular entity, and the processors field contains the name of components that altered this specific entity.

{
  "text": "show me chinese restaurants",
  "intent": "restaurant_search",
  "entities": [
    {
      "start": 8,
      "end": 15,
      "value": "chinese",
      "entity": "cuisine",
      "extractor": "CRFEntityExtractor",
      "confidence": 0.854,
      "processors": []
    }
  ]
}

Some extractors, like duckling, may include additional information.

Custom Entities

Almost every chatbot and voice app will have some custom entities. A restaurant assistant should understand chinese as a cuisine, but to a language-learning assistant it would mean something very different. The CRFEntityExtractor and the DIETClassifier component can learn custom entities in any language, given some training data.

Entities Roles and Groups

Warning: This feature is experimental. We introduce experimental features to get feedback from our community, so we encourage you to try it out! However, the functionality might be changed or removed in the future.

Assigning custom entity labels to words, allow you to define certain concepts in the data. For example:

I want to fly from [Berlin](city) to [San Francisco](city).

Extracting Places, Dates, People, Organizations

spaCy has excellent pre-trained named-entity recognizers for a few different languages.

Dates, Amounts of Money, Durations, Distances, Ordinals

The duckling library does a great job of turning expressions like “next Thursday at 8pm” into actual datetime objects that you can use.

Passing Custom Features to CRFEntityExtractor

If you want to pass custom features, such as pre-trained word embeddings, to CRFEntityExtractor, you can add any dense featurizer to the pipeline before the CRFEntityExtractor.