There are a number of different entity extraction components, which can seem intimidating for new users. Here we’ll go through a few use cases and make recommendations of what to use.
||MITIE||structured SVM||good for training custom entities|
||pycrfsuite||conditional random field||good for training custom entities|
||spaCy||averaged perceptron||provides pre-trained entities|
||duckling||context-free grammar||provides pre-trained entities|
To use these components, you will probably want to define a custom pipeline, see Processing Pipeline. You can add multiple ner components to your pipeline; the results from each will be combined in the final output.
Here we’ll outline some common use cases for entity extraction, and make recommendations on which components to use.
Places, Dates, People, Organisations¶
spaCy has excellent pre-trained named-entity recognisers in a number of models. You can test them out in this awesome interactive demo. 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¶
The duckling package does a great job of turning expressions like “next Thursday at 8pm” into actual datetime objects that you can use. It can also handle durations like “two hours”, amounts of money, distances, etc. Fortunately, there is also a python wrapper for duckling! You can use this component by installing the duckling package from PyPI and adding
ner_duckling to your pipeline.
Custom, Domain-specific entities¶
In the introductory tutorial we build a restaurant bot, and create custom entities for location and cuisine.
The best components for training these domain-specific entity recognisers are the
It is recommended that you experiment with both of these to see what works best for your data set.