sent-pattern 0.0.9

project that interpret English sentences to improve your ability to read English sentences correctly.

Sent Pattern

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

Quick Start

Installation

pip install sent-pattern

Usage

import 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)
elements = tags.create_elements(dep_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)
custom = ElementsFactory.make_custom_elements(dep_list, doc=doc, option="prep")
phrase = custom.option

print(phrase.prep_groups)
# [of service instance registration and deregistration]

License

Distributed under the terms of the MIT license, "sent-pattern" is free and open source software