> For the complete documentation index, see [llms.txt](https://lauradang.gitbook.io/notes/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lauradang.gitbook.io/notes/machine-learning/rasa/optimizing-entity-recognition.md).

# Optimizing Entity Recognition

### SpaCy ([SpacyEntityExtractor](https://rasa.com/docs/rasa/nlu/components/#spacyentityextractor)) - `ner_spacy`

* Pretrained entity extractors
* Statistical BILOU transition model

### Duckling

* Number related information (dates, distance, duration)
* Run server on docker image [ner\_duckling](http://rasa.com/docs/rasa/nlu/components/#ducklinghttpextractor)

### NER\_CRF ([CRFEntityExtractor](https://rasa.com/docs/rasa/nlu/components/#crfentityextractor))

* trained from scratch
* Need to annotate training data yourself
* Annotate training examples EVERYWHERE in training data (even if entity is not relevant for intent)
* Use of lookup tables makes `ner_crf` prone to overfitting
  * If training data matches Regex or Lookup, it will ignore other features, so if you have message with entity that is not matched by Regex, `ner_crf` will not detect

## Common Problems

### Entities are not recognizing unseen values

* Could be:
  * Lack of training data
  * Overfitting of `ner_crf` (try training model without regex or look up)

### Map extracted entity to different value

* [ner\_synonyms](http://rasa.com/docs/rasa/nlu/components/#entitysynonymmapper)


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