#!/usr/bin/env python3

# -*- coding: utf-8 -*-

"""

Build word tree of a sentence using NLP (Spacy) approach

@author: saul

"""

#Tree

from __future__ import unicode_literals, print_function

import plac # Plac is a wrapper over argparse and works in all versions of Python starting from Python 2.3 up to Python 3.1

import spacy

@plac.annotations(model=("Model to load", "positional", None, str))

def main(model="en_core_web_sm"):

nlp = spacy.load(model)

print("Loaded model '%s'" % model)

doc = nlp(

"displaCy uses CSS and JavaScript to show you how computers "

"understand language"

)

# The easiest way is to find the head of the subtree you want, and then use

# the `.subtree`, `.children`, `.lefts` and `.rights` iterators. `.subtree`

# is the one that does what you're asking for most directly:

for word in doc:

if word.dep_ in ("xcomp", "ccomp"):

print("".join(w.text_with_ws for w in word.subtree))

# It'd probably be better for `word.subtree` to return a `Span` object

# instead of a generator over the tokens. If you want the `Span` you can

# get it via the `.right_edge` and `.left_edge` properties. The `Span`

# object is nice because you can easily get a vector, merge it, etc.

for word in doc:

if word.dep_ in ("xcomp", "ccomp"):

subtree_span = doc[word.left_edge.i : word.right_edge.i + 1]

print(subtree_span.text, "|", subtree_span.root.text)

# You might also want to select a head, and then select a start and end

# position by walking along its children. You could then take the

# `.left_edge` and `.right_edge` of those tokens, and use it to calculate

# a span.

if __name__ == "__main__":

plac.call(main)