This package categorizes English sentences into one of five basic sentence patterns and identifies the subject, verb, object, and other components. The five basic sentence patterns are based on C. T. Onions's Advanced English Syntax and are frequently used when teaching English in Japan.
Influence of His Grammar on English Language Education in Japan
This is Spacy Universe Project
pip install sent-patternimport spacy
nlp = spacy.load("en_core_web_lg")
nlp.add_pipe("span_noun")
nlp.add_pipe("sent_pattern")
text = "he gives me something"
doc = nlp(text)
pattern = doc._.sentpattern
print(pattern)
# FourthSentencePattern (class)
print(pattern.subject.root)
# he (Token)
print(pattern.verb.root)
# give (Token)If you want to know the sentence pattern without using components, we recommend using method directly
import spacy
from sent_pattern import tags
nlp = spacy.load("en_core_web_md")
text = "he gives me something"
doc = nlp(text)
dep_list = tags.create_dep_list(doc)
lemma_list = tags.create_lemma_list(doc)
elements = tags.create_elements(dep_list, lemma_list)
pattern = tags.create_sent_pattern(elements)
print(pattern.subject.root.text)
# he (string)
print(dep_list)
# {'ROOT': [gives], 'dative': [me], 'dobj': [something], 'nsubj': [he]}
print(pattern.abbreviation)
# "SVOO"
how to get prep phrase
nlp = spacy.load("en_core_web_md")
text = "The Eureka client handles all aspects of service instance registration and deregistration"
doc = nlp(text)
dep_list = tags.create_dep_list(doc)
lemma_list = tags.create_lemma_list(doc)
custom = ElementsFactory.make_custom_elements(dep_list, lemma_list, doc=doc, option="prep")
phrase = custom.option
print(phrase.prep_groups)
# [of service instance registration and deregistration]Distributed under the terms of the MIT license, "sent-pattern" is free and open source software