These docs are for version 1.x of Rasa Open Source. ## User Guide - [Installation](https://legacy-docs-v1.rasa.com/1.9.6/user-guide/installation/) - [Tutorial: Rasa Basics](https://legacy-docs-v1.rasa.com/1.9.6/user-guide/rasa-tutorial/) - [Tutorial: Building Assistants](https://legacy-docs-v1.rasa.com/1.9.6/user-guide/building-assistants/) - [Command Line Interface](https://legacy-docs-v1.rasa.com/1.9.6/user-guide/command-line-interface/) - [Architecture](https://legacy-docs-v1.rasa.com/1.9.6/user-guide/architecture/) - [Messaging and Voice Channels](https://legacy-docs-v1.rasa.com/1.9.6/user-guide/messaging-and-voice-channels/) - [Testing Your Assistant](https://legacy-docs-v1.rasa.com/1.9.6/user-guide/testing-your-assistant/) - [Setting up CI/CD](https://legacy-docs-v1.rasa.com/1.9.6/user-guide/setting-up-ci-cd/) - [Validate Data](https://legacy-docs-v1.rasa.com/1.9.6/user-guide/validate-files/) - [Configuring the HTTP API](https://legacy-docs-v1.rasa.com/1.9.6/user-guide/configuring-http-api/) - [Deploying Your Rasa Assistant](https://legacy-docs-v1.rasa.com/1.9.6/user-guide/how-to-deploy/) - [Cloud Storage](https://legacy-docs-v1.rasa.com/1.9.6/user-guide/cloud-storage/) ## NLU - [About](https://legacy-docs-v1.rasa.com/1.9.6/nlu/about/) - [Using NLU Only](https://legacy-docs-v1.rasa.com/1.9.6/nlu/using-nlu-only/) - [Training Data Format](https://legacy-docs-v1.rasa.com/1.9.6/nlu/training-data-format/) - [Language Support](https://legacy-docs-v1.rasa.com/1.9.6/nlu/language-support/) - [Choosing a Pipeline](https://legacy-docs-v1.rasa.com/1.9.6/nlu/choosing-a-pipeline/) - [Components](https://legacy-docs-v1.rasa.com/1.9.6/nlu/components/) - [Entity Extraction](https://legacy-docs-v1.rasa.com/1.9.6/nlu/entity-extraction/#) ## Core - [About](https://legacy-docs-v1.rasa.com/1.9.6/core/about/) - [Stories](https://legacy-docs-v1.rasa.com/1.9.6/core/stories/) - [Domains](https://legacy-docs-v1.rasa.com/1.9.6/core/domains/) - [Responses](https://legacy-docs-v1.rasa.com/1.9.6/core/responses/) - [Actions](https://legacy-docs-v1.rasa.com/1.9.6/core/actions/) - [Reminders and External Events](https://legacy-docs-v1.rasa.com/1.9.6/core/reminders-and-external-events/) - [Policies](https://legacy-docs-v1.rasa.com/1.9.6/core/policies/) - [Slots](https://legacy-docs-v1.rasa.com/1.9.6/core/slots/) - [Forms](https://legacy-docs-v1.rasa.com/1.9.6/core/forms/) - [Retrieval Actions](https://legacy-docs-v1.rasa.com/1.9.6/core/retrieval-actions/) - [Interactive Learning](https://legacy-docs-v1.rasa.com/1.9.6/core/interactive-learning/) - [Fallback Actions](https://legacy-docs-v1.rasa.com/1.9.6/core/fallback-actions/) - [Knowledge Base Actions](https://legacy-docs-v1.rasa.com/1.9.6/core/knowledge-bases/) ## Conversation Design - [Dialogue Elements](https://legacy-docs-v1.rasa.com/1.9.6/dialogue-elements/dialogue-elements/) - [Small Talk](https://legacy-docs-v1.rasa.com/1.9.6/dialogue-elements/small-talk/) - [Completing Tasks](https://legacy-docs-v1.rasa.com/1.9.6/dialogue-elements/completing-tasks/) - [Guiding Users](https://legacy-docs-v1.rasa.com/1.9.6/dialogue-elements/guiding-users/) ## API Reference - [Action Server](https://legacy-docs-v1.rasa.com/1.9.6/api/action-server/) - [HTTP API](https://legacy-docs-v1.rasa.com/1.9.6/api/http-api/) - [Jupyter Notebooks](https://legacy-docs-v1.rasa.com/1.9.6/api/jupyter-notebooks/) - [Agent](https://legacy-docs-v1.rasa.com/1.9.6/api/agent/) - [Custom NLU Components](https://legacy-docs-v1.rasa.com/1.9.6/api/custom-nlu-components/) - [Rasa SDK](https://legacy-docs-v1.rasa.com/1.9.6/api/rasa-sdk/) - [Events](https://legacy-docs-v1.rasa.com/1.9.6/api/events/) - [Tracker](https://legacy-docs-v1.rasa.com/1.9.6/api/tracker/) - [Tracker Stores](https://legacy-docs-v1.rasa.com/1.9.6/api/tracker-stores/) - [Event Brokers](https://legacy-docs-v1.rasa.com/1.9.6/api/event-brokers/) - [Lock Stores](https://legacy-docs-v1.rasa.com/1.9.6/api/lock-stores/) - [Training Data Importers](https://legacy-docs-v1.rasa.com/1.9.6/api/training-data-importers/) - [Featurization of Conversations](https://legacy-docs-v1.rasa.com/1.9.6/api/core-featurization/) - [TensorFlow Configuration](https://legacy-docs-v1.rasa.com/1.9.6/api/tensorflow_usage/) - [Migration Guide](https://legacy-docs-v1.rasa.com/1.9.6/migration-guide/) - [Rasa Open Source Change Log](https://legacy-docs-v1.rasa.com/1.9.6/changelog/) ## Migrate from (beta) - [Dialogflow](https://legacy-docs-v1.rasa.com/1.9.6/migrate-from/google-dialogflow-to-rasa/) - [Wit.ai](https://legacy-docs-v1.rasa.com/1.9.6/migrate-from/facebook-wit-ai-to-rasa/) - [LUIS](https://legacy-docs-v1.rasa.com/1.9.6/migrate-from/microsoft-luis-to-rasa/) - [IBM Watson](https://legacy-docs-v1.rasa.com/1.9.6/migrate-from/ibm-watson-to-rasa/) ## Reference - [Glossary](https://legacy-docs-v1.rasa.com/1.9.6/glossary/) ## Versions viewing: 1.9.6 # 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/1.9.6/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": [] } ] } ``` ## [Custom Entities](https://legacy-docs-v1.rasa.com/1.9.6/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/1.9.6/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/1.9.6/nlu/entity-extraction/#extracting-places-dates-people-organisations) 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/1.9.6/nlu/entity-extraction/#dates-amounts-of-money-durations-distances-ordinals) 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/1.9.6/nlu/components/#ducklinghttpextractor)). ## [Regular Expressions (regex)](https://legacy-docs-v1.rasa.com/1.9.6/nlu/entity-extraction/#regular-expressions-regex) 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/1.9.6/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. ## [Passing Custom Features to `CRFEntityExtractor`](https://legacy-docs-v1.rasa.com/1.9.6/nlu/entity-extraction/#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. 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.