问题
When using spacy you can easily loop across the noun_phrases of a text as follows:
S='This is an example sentence that should include several parts and also make clear that studying Natural language Processing is not difficult'
nlp = spacy.load('en_core_web_sm')
doc = nlp(S)
[chunk.text for chunk in doc.noun_chunks]
# = ['an example sentence', 'several parts', 'Natural language Processing']
You can also get the "root" of the noun chunk:
[chunk.root.text for chunk in doc.noun_chunks]
# = ['sentence', 'parts', 'Processing']
How can I get the POS of every of those words (even if looks like the root of a noun_phrase is always a noun), and how can I get the lemma, the shape and the word in singular of that particular word.
Is that even possible?
thx.
回答1:
Each chunk.root
is a Token where you can get different attributes including lemma_
and pos_
(or tag_
if you prefer the PennTreekbak POS tags).
import spacy
S='This is an example sentence that should include several parts and also make ' \
'clear that studying Natural language Processing is not difficult'
nlp = spacy.load('en_core_web_sm')
doc = nlp(S)
for chunk in doc.noun_chunks:
print('%-12s %-6s %s' % (chunk.root.text, chunk.root.pos_, chunk.root.lemma_))
sentence NOUN sentence
parts NOUN part
Processing NOUN processing
BTW... In this sentence "processing" is a noun so the lemma of it is "processing", not "process" which is the lemma of the verb "processing".
来源:https://stackoverflow.com/questions/62272958/finding-the-pos-of-the-root-of-a-noun-chunk-with-spacy