# 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<br>on top of a transformer | good for training custom entities     |

- [The “entity” Object](https://legacy-docs-v1.rasa.com/1.9.4/nlu/entity-extraction/#the-entity-object)
- [Custom Entities](https://legacy-docs-v1.rasa.com/1.9.4/nlu/entity-extraction/#custom-entities)
- [Extracting Places, Dates, People, Organisations](https://legacy-docs-v1.rasa.com/1.9.4/nlu/entity-extraction/#extracting-places-dates-people-organisations)
- [Dates, Amounts of Money, Durations, Distances, Ordinals](https://legacy-docs-v1.rasa.com/1.9.4/nlu/entity-extraction/#dates-amounts-of-money-durations-distances-ordinals)
- [Regular Expressions (regex)](https://legacy-docs-v1.rasa.com/1.9.4/nlu/entity-extraction/#regular-expressions-regex)
- [Passing Custom Features to `CRFEntityExtractor`](https://legacy-docs-v1.rasa.com/1.9.4/nlu/entity-extraction/#passing-custom-features-to-crfentityextractor)

## [The “entity” Object](https://legacy-docs-v1.rasa.com/1.9.4/nlu/entity-extraction/#id2)

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 to 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](https://legacy-docs-v1.rasa.com/1.9.4/nlu/entity-extraction/#id3)

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` component can learn custom entities in any language, given some training data. See [Training Data Format](https://legacy-docs-v1.rasa.com/1.9.4/nlu/training-data-format/#training-data-format) for details on how to include entities in your training data.

## [Extracting Places, Dates, People, Organisations](https://legacy-docs-v1.rasa.com/1.9.4/nlu/entity-extraction/#id4)

spaCy has excellent pre-trained named-entity recognisers for a few different languages. You can test them out in this [interactive demo](https://demos.explosion.ai/displacy-ent/). We don’t recommend that you try to train your own NER using spaCy, unless you have a lot of data and know what you are doing. Note that some spaCy models are highly case-sensitive.

## [Dates, Amounts of Money, Durations, Distances, Ordinals](https://legacy-docs-v1.rasa.com/1.9.4/nlu/entity-extraction/#id5)

The [duckling](https://duckling.wit.ai/) library does a great job of turning expressions like “next Thursday at 8pm” into actual datetime objects that you can use, e.g.

"next Thursday at 8pm"
    => {"value":"2018-05-31T20:00:00.000+01:00"}

The list of supported languages can be found [here](https://github.com/facebook/duckling/tree/master/Duckling/Dimensions). Duckling can also handle durations like “two hours”, amounts of money, distances, and ordinals. Fortunately, there is a duckling docker container ready to use, that you just need to spin up and connect to Rasa NLU (see [DucklingHTTPExtractor](https://legacy-docs-v1.rasa.com/1.9.4/nlu/components/#ducklinghttpextractor)).

## [Regular Expressions (regex)](https://legacy-docs-v1.rasa.com/1.9.4/nlu/entity-extraction/#id6)

You can use regular expressions to help the CRF model learn to recognize entities. In your training data (see [Training Data Format](https://legacy-docs-v1.rasa.com/1.9.4/nlu/training-data-format/#training-data-format)) you can provide a list of regular expressions, each of which provides the `CRFEntityExtractor` with an extra binary feature, which says if the regex was found (1) or not (0).

For example, the names of German streets often end in `strasse`. By adding this as a regex, we are telling the model to pay attention to words ending this way and will quickly learn to associate that with a location entity.

If you just want to match regular expressions exactly, you can do this in your code, as a post-processing step after receiving the response from Rasa NLU.

## [Passing Custom Features to `CRFEntityExtractor`](https://legacy-docs-v1.rasa.com/1.9.4/nlu/entity-extraction/#id7)

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. A warning will be shown in case the check fails. However, `CRFEntityExtractor` will continue to train just without the additional custom features. In case dense features are present, `CRFEntityExtractor` will pass the dense features to `sklearn_crfsuite` and use them for training.