Split a sentence using nltk and python

Question

I am working on a task which involves information extraction, for which I require splitting a complex sentence into a bunch of simple sentences. For instance,

In optics a ray is an idealized model of light, obtained by choosing a line that is perpendicular to the wavefronts of the actual light, and that points in the direction of energy flow.

may be split as

• ray is an idealized model of light.
• ray is obtained by choosing a line that is perpendicular to wavefronts of actual light.
• ray points in the direction of energy flow.

The only condition being, each simple form must in SVO (Subject-Verb-Object) format. For example for sentence ray is an idealized model of light

Subject - ray
Verb - is
Object - idealized model of light

What approach should I take? I am using NLTK and python 2.7. Can dependency parsing be of any help?

Answer 1

If you are familiar with spacy, you can use the dependency of the words in the sentence:

import spacy

nlp = spacy.load("en_core_web_sm")
doc = nlp("Apple is looking at buying U.K. startup for $1 billion")

for token in doc:
    print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
            token.shape_, token.is_alpha, token.is_stop)

token.dep_ can give you the root ( verb) and the relations in the sentence.

DEPENDENCY EXAMPLE

import spacy
from spacy import displacy

nlp = spacy.load("en_core_web_sm")
doc = nlp("This is a sentence.")
displacy.serve(doc, style="dep")

displacy helps visualizing the relations in the sentences.

You can check https://spacy.io/ for more information.