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I've a problem where I should create a custom NER by using sklearn CRF. In the official tutorial, they are using CoNLL2002 corpus is available in NLTK where the entities are represented with a single word but in my problem, an entity can be formed with multiple words ex: United States of America, Cinema at Miami, etc.

Can CRF handle this?

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Absolutely. If you look at the training tutorial, it implies that this isn't an issue at all. When using multi-word entities, you typically need to use a IOB or BILUO tagging schemes, which helps your model train better.

But from a mathematical perspective, there aren't any restrictions for a CRF, as CRF models the likelihood of particular sequences/transitions. Often, people set restrictions for particular transitions if you know in advance that they aren't possible. But by default, all transitions are allowed. In sklearn-crf, allowing all transitions is done by using the all_possible_transitions=True argument.

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