spaCy has an 'ner' pipeline component that identifies token spans fitting a predetermined set of named entities. These are available as the ents property of a Doc object.

import spacy
nlp = spacy.load('en_core_web_sm')

def show_ents(doc):
    if doc.ents:
        for ent in doc.ents:
            print(ent.text+' - '+ent.label_+' - '+str(spacy.explain(ent.label_)))
    else:
        print('No named entities found.')

doc = nlp(u'May I go to Washington, DC next May to see the Washington Monument?')

show_ents(doc)

Washington, DC - GPE - Countries, cities, states
next May - DATE - Absolute or relative dates or periods
the Washington Monument - ORG - Companies, agencies, institutions, etc.

Here we see tokens combine to form the entities Washington, DC, next May and the Washington Monument

Entity annotations

Doc.ents are token spans with their own set of annotations.

`ent.text`	The original entity text
`ent.label`	The entity type's hash value
`ent.label_`	The entity type's string description
`ent.start`	The token span's start index position in the Doc
`ent.end`	The token span's stop index position in the Doc
`ent.start_char`	The entity text's start index position in the Doc
`ent.end_char`	The entity text's stop index position in the Doc

doc = nlp(u'Can I please borrow 500 dollars from you to buy some Microsoft stock?')

for ent in doc.ents:
    print(ent.text, ent.start, ent.end, ent.start_char, ent.end_char, ent.label_)

500 dollars 4 6 20 31 MONEY
Microsoft 11 12 53 62 ORG

NER Tags

Tags are accessible through the .label_ property of an entity.

TYPE	DESCRIPTION	EXAMPLE
`PERSON`	People, including fictional.	Fred Flintstone
`NORP`	Nationalities or religious or political groups.	The Republican Party
`FAC`	Buildings, airports, highways, bridges, etc.	Logan International Airport, The Golden Gate
`ORG`	Companies, agencies, institutions, etc.	Microsoft, FBI, MIT
`GPE`	Countries, cities, states.	France, UAR, Chicago, Idaho
`LOC`	Non-GPE locations, mountain ranges, bodies of water.	Europe, Nile River, Midwest
`PRODUCT`	Objects, vehicles, foods, etc. (Not services.)	Formula 1
`EVENT`	Named hurricanes, battles, wars, sports events, etc.	Olympic Games
`WORK_OF_ART`	Titles of books, songs, etc.	The Mona Lisa
`LAW`	Named documents made into laws.	Roe v. Wade
`LANGUAGE`	Any named language.	English
`DATE`	Absolute or relative dates or periods.	20 July 1969
`TIME`	Times smaller than a day.	Four hours
`PERCENT`	Percentage, including "%".	Eighty percent
`MONEY`	Monetary values, including unit.	Twenty Cents
`QUANTITY`	Measurements, as of weight or distance.	Several kilometers, 55kg
`ORDINAL`	"first", "second", etc.	9th, Ninth
`CARDINAL`	Numerals that do not fall under another type.	2, Two, Fifty-two

Adding a Named Entity to a Span

Normally we would have spaCy build a library of named entities by training it on several samples of text.
In this case, we only want to add one value:

doc = nlp(u'Tesla to build a U.K. factory for $6 million')

show_ents(doc)

U.K. - GPE - Countries, cities, states
$6 million - MONEY - Monetary values, including unit

Right now, spaCy does not recognize "Tesla" as a company.

from spacy.tokens import Span

  ### Get the hash value of the ORG entity label
ORG = doc.vocab.strings[u'ORG'] 

ORG

383

new_ent = Span(doc, 0, 1, label=ORG)

new_ent

Tesla

doc.ents = list(doc.ents) + [new_ent]

In the code above, the arguments passed to `Span()` are:

doc - the name of the Doc object
0 - the start index position of the span
1 - the stop index position (exclusive)
label=ORG - the label assigned to our entity

show_ents(doc)

Tesla - ORG - Companies, agencies, institutions, etc.
U.K. - GPE - Countries, cities, states
$6 million - MONEY - Monetary values, including unit

Adding Named Entities to All Matching Spans

What if we want to tag all occurrences of "Tesla"? In this section we show how to use the PhraseMatcher to identify a series of spans in the Doc:

doc = nlp(u'Our company plans to introduce a new vacuum cleaner. '
          u'If successful, the vacuum cleaner will be our first product.')

show_ents(doc)

first - ORDINAL - "first", "second", etc.

Step 1 - Import PhraseMatcher and create a matcher object:

from spacy.matcher import PhraseMatcher
matcher = PhraseMatcher(nlp.vocab)

Step 2 - Create the desired phrase patterns:

phrase_list = ['vacuum cleaner', 'vacuum-cleaner']
phrase_patterns = [nlp(text) for text in phrase_list]

Step 3 -Apply the patterns to our matcher object:

matcher.add('newproduct', None, *phrase_patterns)

Step 4 - Apply the matcher to our Doc object:

matches = matcher(doc)

### See what matches occur:
matches

[(2689272359382549672, 7, 9), (2689272359382549672, 14, 16)]

**Step 4** - Creating a Spans from each and creating named entity for each.

from spacy.tokens import Span

PROD = doc.vocab.strings[u'PRODUCT']

new_ents = [Span(doc, match[1],match[2],label=PROD) for match in matches]

doc.ents = list(doc.ents) + new_ents

show_ents(doc)

vacuum cleaner - PRODUCT - Objects, vehicles, foods, etc. (not services)
vacuum cleaner - PRODUCT - Objects, vehicles, foods, etc. (not services)
first - ORDINAL - "first", "second", etc.

Counting Entities

While spaCy may not have a built-in tool for counting entities, we can pass a conditional statement into a list comprehension:

doc = nlp(u'Originally priced at $29.50, the sweater was marked down to five dollars.')

show_ents(doc)

29.50 - MONEY - Monetary values, including unit
five dollars - MONEY - Monetary values, including unit

len([ent for ent in doc.ents if ent.label_=='MONEY'])

2

For more on Named Entity Recognition visit https://spacy.io/usage/linguistic-features#101

Noun Chunks

Doc.noun_chunks are base noun phrases: token spans that include the noun and words describing the noun. Noun chunks cannot be nested, cannot overlap, and do not involve prepositional phrases or relative clauses.
Where Doc.ents rely on the ner pipeline component, Doc.noun_chunks are provided by the parser.

`noun_chunks` components:

`.text`	The original noun chunk text.
`.root.text`	The original text of the word connecting the noun chunk to the rest of the parse.
`.root.dep_`	Dependency relation connecting the root to its head.
`.root.head.text`	The text of the root token's head.

doc = nlp(u"Autonomous cars shift insurance liability toward manufacturers.")

for chunk in doc.noun_chunks:
    print(chunk.text+' - '+chunk.root.text+' - '+chunk.root.dep_+' - '+chunk.root.head.text)

Autonomous cars - cars - nsubj - shift
insurance liability - liability - dobj - shift
manufacturers - manufacturers - pobj - toward

`Doc.noun_chunks` is a generator function

Previously we mentioned that Doc objects do not retain a list of sentences, but they're available through the Doc.sents generator.
It's the same with Doc.noun_chunks - lists can be created if needed:

len(doc.noun_chunks)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
C:\Users\VICKY~1.CRA\AppData\Local\Temp/ipykernel_10304/1545862379.py in <module>
----> 1 len(doc.noun_chunks)

TypeError: object of type 'generator' has no len()

len(list(doc.noun_chunks))

3

For more on noun_chunks visit https://spacy.io/usage/linguistic-features#noun-chunks