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.

The use of synonyms can cause the value field not match the text exactly. Instead it will return the trained synonym.

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

Note: The confidence will be set by the CRFEntityExtractor component. The DucklingHTTPExtractor will always return 1. The SpacyEntityExtractor extractor and DIETClassifier do not provide this information and return null.

Some extractors, like duckling, may include additional information. For example:

{
  "additional_info":{
    "grain":"day",
    "type":"value",
    "value":"2018-06-21T00:00:00.000-07:00",
    "values":[
      {
        "grain":"day",
        "type":"value",
        "value":"2018-06-21T00:00:00.000-07:00"
      }
    ]
  },
  "confidence":1.0,
  "end":5,
  "entity":"time",
  "extractor":"DucklingHTTPExtractor",
  "start":0,
  "text":"today",
  "value":"2018-06-21T00:00:00.000-07:00"
}

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. See Training Data Format for details on how to include entities in your training data.

Entities Roles and Groups

Assigning custom entity labels to words, allow you to define certain concepts in the data. For example, we can define what a city is:

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

However, sometimes you want to specify entities even further.

- I want to fly from [Berlin]{"entity": "city", "role": "departure"} to [San Francisco]{"entity": "city", "role": "destination"}.

You can also group different entities by specifying a group label next to the entity label. The group label can, for example, be used to define different orders. In the following example we use the group label to reference what toppings goes with which pizza and what size which pizza has.

Give me a [small]{"entity": "size", "group": "1"} pizza with [mushrooms]{"entity": "topping", "group": "1"} and a [large]{"entity": "size", "group": "2"} [pepperoni]{"entity": "topping", "group": "2"}

See Training Data Format for details on how to define entities with roles and groups in your training data.

The entity object returned by the extractor will include the detected role/group label.

{
  "text": "Book a flight from Berlin to SF",
  "intent": "book_flight",
  "entities": [
    {
      "start": 19,
      "end": 25,
      "value": "Berlin",
      "entity": "city",
      "role": "departure",
      "extractor": "DIETClassifier"
    },
    {
      "start": 29,
      "end": 31,
      "value": "San Francisco",
      "entity": "city",
      "role": "destination",
      "extractor": "DIETClassifier"
    }
  ]
}

Extracting Places, Dates, People, Organizations

spaCy has excellent pre-trained named-entity recognizers for a few different languages. You can test them out in this interactive demo.

Dates, Amounts of Money, Durations, Distances, Ordinals

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

Regular Expressions (regex)

You can use regular expressions to help the CRF model learn to recognize entities.

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. CRFEntityExtractor automatically finds the additional dense features and checks if the dense features are an iterable of len(tokens), where each entry is a vector.