:py:mod:`medcat.ner.vocab_based_annotator`
==========================================

.. py:module:: medcat.ner.vocab_based_annotator

.. autoapi-nested-parse::

   I would just ignore this whole class, it's just a lot of rules that work nicely for CDB
   once the software is trained the main thing are the context vectors.



Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   medcat.ner.vocab_based_annotator.maybe_annotate_name



Attributes
~~~~~~~~~~

.. autoapisummary::

   medcat.ner.vocab_based_annotator.logger


.. py:data:: logger

   

.. py:function:: maybe_annotate_name(name, tkns, doc, cdb, config, label = 'concept')

   Given a name it will check should it be annotated based on config rules. If yes
   the annotation will be added to the doc._.ents array.

   :param name: The name found in the text of the document.
   :type name: str
   :param tkns: Tokens that belong to this name in the spacy document.
   :type tkns: List[spacy.tokens.Token]
   :param doc: Spacy document to be annotated with named entities.
   :type doc: Doc
   :param cdb: Concept database.
   :type cdb: CDB
   :param config: Global config for medcat.
   :type config: Config
   :param label: Label for this name (usually `concept` if we are using a vocab based approach).
   :type label: str

   :Returns: **Optional[Span]** -- The Span, if relevant.