src.dackar.pipelines.PhraseEntityMatcher
========================================

.. py:module:: src.dackar.pipelines.PhraseEntityMatcher


Attributes
----------

.. autoapisummary::

   src.dackar.pipelines.PhraseEntityMatcher.logger


Classes
-------

.. autoapisummary::

   src.dackar.pipelines.PhraseEntityMatcher.PhraseEntityMatcher


Functions
---------

.. autoapisummary::

   src.dackar.pipelines.PhraseEntityMatcher.create_phrase_matcher_component


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

.. py:data:: logger

.. py:function:: create_phrase_matcher_component(nlp, name, label, patterns, asSpan)

.. py:class:: PhraseEntityMatcher(nlp, label, patterns, asSpan=True, callback=None)

   Bases: :py:obj:`object`


   Phrase Entity Recognition class

   How to use it:

   .. code-block:: python

     from PhraseEntityMatcher import PhraseEntityMatcher
     nlp = spacy.load("en_core_web_sm")
     phraseList = ["safety cage", "cage", "pump"]
     pmatcher = PhraseEntityMatcher(nlp, 'ssc', phraseList)
     doc = nlp("The shaft deflection is causing the safety cage to rattle. Pumps not experiencing enough flow for the pumps to keep the check valves open during test. Pump not experiencing enough flow during test. Shaft made noise. Vibration seems like it is coming from the shaft.")
     updatedDoc = pmatcher(doc)

   or:

   .. code-block:: python

     nlp.add_pipe('phrase_entity_matcher', config={"label": "ssc", "patterns":["safety cage", "pump"], "asSpan":True})
     newDoc = nlp(doc.text)


   .. py:attribute:: name
      :value: 'phrase_entity_matcher'



   .. py:attribute:: matcher


   .. py:attribute:: asSpan
      :value: True



   .. py:method:: __call__(doc, replace=False)

      :param doc: spacy.tokens.doc.Doc, the processed document using nlp pipelines
      :param replace: if True, relabel duplicated entity with new label
      :type replace: bool