The statistical pipeline components like ner provide their labels under .labels:

import spacy
nlp = spacy.load("en_core_web_sm")
nlp.get_pipe("ner").labels

Hey atalnarayan,

In general the approach you are taking seems to be on the right track, but your question is a bit general for a discussion here. Let me point you to some relevant material:

1. Finding video game titles with sense2vec: https://www.youtube.com/watch?v=EoYHbUHr0fM
2. Detailed example about using the entity ruler to find museum names: https://www.youtube.com/watch?v=Ds18bQAzygo.
3. Rather than the EntityRuler we recommend using the SpanRuler in the future: https://spacy.io/api/spanruler
4. Using dependency tree for extracting information: https://www.youtube.com/watch?v=BoyLPiXXEYA&t=429s.
5. For more in-depth information about entity extraction I recommend this Chapter: https://web.stanford.edu/~jurafsky/slp3/8.pdf
6. For practical examples for machine learning based named entity recognition with spacy you can checkout the relevant projects here: https://github.com/explosion/projects.

The issue with models accuracy

The problem with all models is that they don't have 100% accuracy and even using a bigger model doesn't help to recognize dates. Here are the accuracy values (F-score, precision, recall) for NER models--they are all around 86%.

document_string = """ 
Electronically signed : Wes Scott, M.D.; Jun 26 2010 11:10AM CST 
 The patient was referred by Dr. Jacob Austin.   
Electronically signed by Robert Clowson, M.D.; Janury 15 2015 11:13AM CST 
Electronically signed by Dr. John Douglas, M.D.; Jun 16 2017 11:13AM CST 
The patient was referred by 
Dr. Jayden Green Olivia.   
"""  

With small model two date items are labelled as 'PERSON':

import spacy                                                                                                                            

nlp = spacy.load('en')                                                                                                                  
sents = nlp(document_string) 
 [ee for ee in sents.ents if ee.label_ == 'PERSON']                                                                                      
# Out:
# [Wes Scott,
#  Jun 26,
#  Jacob Austin,
#  Robert Clowson,
#  John Douglas,
#  Jun 16 2017,
#  Jayden Green Olivia]

With a larger model en_core_web_md the results are even worse in terms of precision, as there are three misclassified entities.

nlp = spacy.load('en_core_web_md')                                                                                                                  
sents = nlp(document_string) 
# Out:
#[Wes Scott,
# Jun 26,
# Jacob Austin,
# Robert Clowson,
# Janury,
# John Douglas,
# Jun 16 2017,
# Jayden Green Olivia]

I also tried other models (xx_ent_wiki_sm, en_core_web_md) and they don't bring any improvement as well.

What about using rules to improve accuracy?

In the small example not only the document seems to have a clear structure, but the misclassified entities are all dates. So why not combine the initial model with a rule-based component?

The good news is that in Spacy:

it's possible can combine statistical and rule-based components in a variety of ways. Rule-based components can be used to improve the accuracy of statistical models

(from https://spacy.io/usage/rule-based-matching#models-rules)

So, by following the example and using the dateparser library (a parser for human readable dates) I've put together a rule-based component that works very well on this example:

from spacy.tokens import Span
import dateparser

def expand_person_entities(doc):
    new_ents = []
    for ent in doc.ents:
        # Only check for title if it's a person and not the first token
        if ent.label_ == "PERSON":
            if ent.start != 0:
                # if person preceded by title, include title in entity
                prev_token = doc[ent.start - 1]
                if prev_token.text in ("Dr", "Dr.", "Mr", "Mr.", "Ms", "Ms."):
                    new_ent = Span(doc, ent.start - 1, ent.end, label=ent.label)
                    new_ents.append(new_ent)
                else:
                    # if entity can be parsed as a date, it's not a person
                    if dateparser.parse(ent.text) is None:
                        new_ents.append(ent) 
        else:
            new_ents.append(ent)
    doc.ents = new_ents
    return doc

# Add the component after the named entity recognizer
# nlp.remove_pipe('expand_person_entities')
nlp.add_pipe(expand_person_entities, after='ner')

doc = nlp(document_string)
[(ent.text, ent.label_) for ent in doc.ents if ent.label_=='PERSON']
# Out:
# [(‘Wes Scott', 'PERSON'),
#  ('Dr. Jacob Austin', 'PERSON'),
#  ('Robert Clowson', 'PERSON'),
#  ('Dr. John Douglas', 'PERSON'),
#  ('Dr. Jayden Green Olivia', 'PERSON')]