Spacy: How to get all words that describe a noun?

Question

I am new to spacy and to nlp overall.

To understand how spacy works I would like to create a function which takes a sentence and returns a dictionary,tuple or list with the noun and the words describing it.

I know that spacy creates a tree of the sentence and knows the use of each word (shown in displacy).

But what's the right way to get from:

"A large room with two yellow dishwashers in it"

To:

{noun:"room",adj:"large"} {noun:"dishwasher",adj:"yellow",adv:"two"}

Or any other solution that gives me all related words in a usable bundle.

Thanks in advance!

Answer 1

This is a very straightforward use of the DependencyMatcher.

import spacy
from spacy.matcher import DependencyMatcher

nlp = spacy.load("en_core_web_sm")

pattern = [
  {
    "RIGHT_ID": "target",
    "RIGHT_ATTRS": {"POS": "NOUN"}
  },
  # founded -> subject
  {
    "LEFT_ID": "target",
    "REL_OP": ">",
    "RIGHT_ID": "modifier",
    "RIGHT_ATTRS": {"DEP": {"IN": ["amod", "nummod"]}}
  },
]

matcher = DependencyMatcher(nlp.vocab)
matcher.add("FOUNDED", [pattern])

text = "A large room with two yellow dishwashers in it"
doc = nlp(text)
for match_id, (target, modifier) in matcher(doc):
    print(doc[modifier], doc[target], sep="\t")

Output:

large   room
two dishwashers
yellow  dishwashers

It should be easy to turn that into a dictionary or whatever you'd like. You might also want to modify it to take proper nouns as the target, or to support other kinds of dependency relations, but this should be a good start.

You may also want to look at the noun chunks feature.

Answer 2

What you want to do is called "noun chunks":

import spacy
nlp = spacy.load('en_core_web_md')
txt = "A large room with two yellow dishwashers in it"
doc = nlp(txt)

chunks = []
for chunk in doc.noun_chunks:
    out = {}
    root = chunk.root
    out[root.pos_] = root
    for tok in chunk:
        if tok != root:
            out[tok.pos_] = tok
    chunks.append(out)
print(chunks)

---

[
 {'NOUN': room, 'DET': A, 'ADJ': large}, 
 {'NOUN': dishwashers, 'NUM': two, 'ADJ': yellow}, 
 {'PRON': it}
]

You may notice "noun chunk" doesn't guarantee the root will always be a noun. Should you wish to restrict your results to nouns only:

chunks = []
for chunk in doc.noun_chunks:
    out = {}
    noun = chunk.root
    if noun.pos_ != 'NOUN':
        continue
    out['noun'] = noun
    for tok in chunk:
        if tok != noun:
            out[tok.pos_] = tok
    chunks.append(out)
    
print(chunks)

---

[
 {'noun': room, 'DET': A, 'ADJ': large}, 
 {'noun': dishwashers, 'NUM': two, 'ADJ': yellow}
]