# 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](https://legacy-docs-v1.rasa.com/1.9.7/nlu/entity-extraction/#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. ```json { "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 returns `null`. Some extractors, like `duckling`, may include additional information. For example: ```json { "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/1.9.7/nlu/entity-extraction/#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` component can learn custom entities in any language, given some training data. See [Training Data Format](https://legacy-docs-v1.rasa.com/1.9.7/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/1.9.7/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/1.9.7/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. ```json "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/1.9.7/nlu/components/#ducklinghttpextractor)). ## [Regular Expressions (regex)](https://legacy-docs-v1.rasa.com/1.9.7/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/1.9.7/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 postprocessing step after receiving the response from Rasa NLU. ## [Passing Custom Features to `CRFEntityExtractor`](https://legacy-docs-v1.rasa.com/1.9.7/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. 👋 I can help you get started with Rasa and answer your technical questions.