How to get the dependency tree with spaCy?

I also needed to do it so below full code:

import sys
def showTree(sent):
    def __showTree(token):
        sys.stdout.write("{")
        [__showTree(t) for t in token.lefts]
        sys.stdout.write("%s->%s(%s)" % (token,token.dep_,token.tag_))
        [__showTree(t) for t in token.rights]
        sys.stdout.write("}")
    return __showTree(sent.root)

And if you want spacing for the terminal:

def showTree(sent):
    def __showTree(token, level):
        tab = "t" * level
        sys.stdout.write("n%s{" % (tab))
        [__showTree(t, level+1) for t in token.lefts]
        sys.stdout.write("n%st%s [%s] (%s)" % (tab,token,token.dep_,token.tag_))
        [__showTree(t, level+1) for t in token.rights]
        sys.stdout.write("n%s}" % (tab))
    return __showTree(sent.root, 1)

In case someone wants to easily view the dependency tree produced by spacy, one solution would be to convert it to an nltk.tree.Tree and use the nltk.tree.Tree.pretty_print method. Here is an example:

import spacy
from nltk import Tree


en_nlp = spacy.load('en')

doc = en_nlp("The quick brown fox jumps over the lazy dog.")

def to_nltk_tree(node):
    if node.n_lefts + node.n_rights > 0:
        return Tree(node.orth_, [to_nltk_tree(child) for child in node.children])
    else:
        return node.orth_


[to_nltk_tree(sent.root).pretty_print() for sent in doc.sents]

Output:

                jumps                  
  ________________|____________         
 |    |     |     |    |      over     
 |    |     |     |    |       |        
 |    |     |     |    |      dog      
 |    |     |     |    |    ___|____    
The quick brown  fox   .  the      lazy

Edit: For changing the token representation you can do this:

def tok_format(tok):
    return "_".join([tok.orth_, tok.tag_])


def to_nltk_tree(node):
    if node.n_lefts + node.n_rights > 0:
        return Tree(tok_format(node), [to_nltk_tree(child) for child in node.children])
    else:
        return tok_format(node)

Which results in:

                         jumps_VBZ                           
   __________________________|___________________             
  |       |        |         |      |         over_IN        
  |       |        |         |      |            |            
  |       |        |         |      |          dog_NN        
  |       |        |         |      |     _______|_______     
The_DT quick_JJ brown_JJ   fox_NN  ._. the_DT         lazy_JJ

The tree isn’t an object in itself; you just navigate it via the relationships between tokens. That’s why the docs talk about navigating the tree, but not ‘getting’ it.

First, let’s parse some text to get a Doc object:

>>> import spacy
>>> nlp = spacy.load('en_core_web_sm')
>>> doc = nlp('First, I wrote some sentences. Then spaCy parsed them. Hooray!')

doc is a Sequence of Token objects:

>>> doc[0]
First
>>> doc[1]
,
>>> doc[2]
I
>>> doc[3]
wrote

But it doesn’t have a single root token. We parsed a text made up of three sentences, so there are three distinct trees, each with their own root. If we want to start our parsing from the root of each sentence, it will help to get the sentences as distinct objects, first. Fortunately, doc exposes these to us via the .sents property:

>>> sentences = list(doc.sents)
>>> for sentence in sentences:
...     print(sentence)
... 
First, I wrote some sentences.
Then spaCy parsed them.
Hooray!

Each of these sentences is a Span with a .root property pointing to its root token. Usually, the root token will be the main verb of the sentence (although this may not be true for unusual sentence structures, such as sentences without a verb):

>>> for sentence in sentences:
...     print(sentence.root)
... 
wrote
parsed
Hooray

With the root token found, we can navigate down the tree via the .children property of each token. For instance, let’s find the subject and object of the verb in the first sentence. The .dep_ property of each child token describes its relationship with its parent; for instance a dep_ of 'nsubj' means that a token is the nominal subject of its parent.

>>> root_token = sentences[0].root
>>> for child in root_token.children:
...     if child.dep_ == 'nsubj':
...         subj = child
...     if child.dep_ == 'dobj':
...         obj = child
... 
>>> subj
I
>>> obj
sentences

We can likewise keep going down the tree by viewing one of these token’s children:

>>> list(obj.children)
[some]

Thus with the properties above, you can navigate the entire tree. If you want to visualise some dependency trees for example sentences to help you understand the structure, I recommend playing with displaCy.

I do not have enough knowledge about the parsing yet. However, outcome of my literature study has resulted in knowing that spaCy has a shift-reduce dependency parsing algorithm. This parses the question/sentence, resulting in a parsing tree. To visualize this, you can use the DisplaCy, combination of CSS and Javascript, works with Python and Cython.
Furthermore, you can parse using the SpaCy library, and import the Natural Language Toolkit (NLTK). Hope this helps

You can use the library below to view your dependency tree, found it extremely helpful!

import spacy
from spacy import displacy

nlp = spacy.load('en')
doc = nlp(u'This is a sentence.')
displacy.serve(doc, style='dep')

You can open it with your browser, and it looks like:

To generate a SVG file:

from pathlib import Path

output_path = Path("yourpath/.svg")
svg = displacy.render(doc, style='dep')
with output_path.open("w", encoding="utf-8") as fh:
    fh.write(svg)

I don’t know if this is a new API call or what, but there’s a .print_tree() method on the Document class that makes quick work of this.

https://spacy.io/api/doc#print_tree

It dumps the dependency tree to JSON. It deals with multiple sentence roots and all that :

import spacy    
nlp = spacy.load('en')
doc1 = nlp(u'This is the way the world ends.  So you say.')  
print(doc1.print_tree(light=True))

The name print_tree is a bit of a misnomer, the method itself doesn’t print anything, rather it returns a list of dicts, one for each sentence.

While the spaCy library has probably changed a bit over the past 5 years, @Mark Amery’s approach works very well. It’s what I’ve been doing to break down sentences within pages and pages of copy in order to get the nominally described features, and the NP or VP’s that are associated with them. Another approach we’ve taken (which may have come to SpaCy in the past 5 years since)… if you look at the root VB in the phrase against your future, and, noting the type of dep, this root’s ancestors and the children of the ancestor, you’ll basically find the heads that point to the subject, object and root. You can break them into clauses that are modifiers based on appositional or conjuncts etc which will tell you if these clauses are supplemental or core to the descriptions of the feature. With that you can rewrite the sentences, which I mainly do to strip out superfluous stuff and create fragments that consist of the hard details. Dunno if helpful to others, but this is the tact I follow after weeks of diagramming nsubj, dobj, conjuncts, and pobjs on paper compared to SpaCy’s tensor based modeling. IMO, it’s worth noting that the tagging done by SpaCy always appears to be 100% correct – every time, even when the fragments are 20 words apart in horribly written run-ons that are horribly written. I’ve never have to second guess it’s output – which is obviously invaluable.

This code can be used to read a spacy dependency parse tree;

import numpy as np
import spacy

rootNodeName = "ROOT"

class ParseNode:
    def __init__(self, token, w):
        self.word = token.text
        self.w = w #or token.i
        self.governor = None
        self.dependentList = []
        self.parseLabel = None
    
def generateSentenceParseNodeList(tokenizedSentence):
    sentenceParseNodeList = []
    for w, token in enumerate(tokenizedSentence):
        print("add parseNode: w = ", w, ": text = ", token.text)
        parseNode = ParseNode(token, w)
        sentenceParseNodeList.append(parseNode)
    return sentenceParseNodeList

def generateDependencyParseTree(tokenizedSentence, sentenceParseNodeList):
    parseTreeHeadNode = None
    for w, tokenDependent in enumerate(tokenizedSentence):
        parseNodeDependent = sentenceParseNodeList[tokenDependent.i]
        print("add dependency relation: tokenDependent w = ", w, ": text = ", tokenDependent.text, ", i = ", tokenDependent.i, ", tag_ = ", tokenDependent.tag_, ", head.text = ", tokenDependent.head.text, ", dep_ = ", tokenDependent.dep_)
        if(tokenDependent.dep_ == rootNodeName):
            parseTreeHeadNode = parseNodeDependent
        else:
            tokenGovernor = tokenDependent.head
            parseNodeGovernor = sentenceParseNodeList[tokenGovernor.i]
            parseNodeDependent.governor = parseNodeGovernor
            parseNodeGovernor.dependentList.append(parseNodeDependent)
            parseNodeDependent.parseLabel = tokenDependent.dep_
    return parseTreeHeadNode

nlp = spacy.load('en_core_web_md')
text = "This is an example sentence."
tokenizedSentence = nlp(text)
sentenceParseNodeList = generateSentenceParseNodeList(tokenizedSentence)
parseTreeHeadNode = generateDependencyParseTree(tokenizedSentence, sentenceParseNodeList)