How to find the shortest dependency path between two words in Python?

Question

I try to find the dependency path between two words in Python given dependency tree.

For sentence

Robots in popular culture are there to remind u

Answer 1

This answer relies on Stanford CoreNLP to obtain the dependency tree of a sentence. It borrows quite some code from HugoMailhot's answer when using networkx.

Before running the code, one needs to:

1. sudo pip install pycorenlp (python interface for Stanford CoreNLP)
2. Download Stanford CoreNLP

3. Start a Stanford CoreNLP server as follows:

   java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 50000
   

Then one can run the following code to find the shortest dependency path between two words:

import networkx as nx
from pycorenlp import StanfordCoreNLP
from pprint import pprint

nlp = StanfordCoreNLP('http://localhost:{0}'.format(9000))
def get_stanford_annotations(text, port=9000,
                             annotators='tokenize,ssplit,pos,lemma,depparse,parse'):
    output = nlp.annotate(text, properties={
        "timeout": "10000",
        "ssplit.newlineIsSentenceBreak": "two",
        'annotators': annotators,
        'outputFormat': 'json'
    })
    return output

# The code expects the document to contains exactly one sentence.
document =  'Robots in popular culture are there to remind us of the awesomeness of'\
            'unbound human agency.'
print('document: {0}'.format(document))

# Parse the text
annotations = get_stanford_annotations(document, port=9000,
                                       annotators='tokenize,ssplit,pos,lemma,depparse')
tokens = annotations['sentences'][0]['tokens']

# Load Stanford CoreNLP's dependency tree into a networkx graph
edges = []
dependencies = {}
for edge in annotations['sentences'][0]['basic-dependencies']:
    edges.append((edge['governor'], edge['dependent']))
    dependencies[(min(edge['governor'], edge['dependent']),
                  max(edge['governor'], edge['dependent']))] = edge

graph = nx.Graph(edges)
#pprint(dependencies)
#print('edges: {0}'.format(edges))

# Find the shortest path
token1 = 'Robots'
token2 = 'awesomeness'
for token in tokens:
    if token1 == token['originalText']:
        token1_index = token['index']
    if token2 == token['originalText']:
        token2_index = token['index']

path = nx.shortest_path(graph, source=token1_index, target=token2_index)
print('path: {0}'.format(path))

for token_id in path:
    token = tokens[token_id-1]
    token_text = token['originalText']
    print('Node {0}\ttoken_text: {1}'.format(token_id,token_text))

The output is:

document: Robots in popular culture are there to remind us of the awesomeness of unbound human agency.
path: [1, 5, 8, 12]
Node 1  token_text: Robots
Node 5  token_text: are
Node 8  token_text: remind
Node 12 token_text: awesomeness

Note that Stanford CoreNLP can be tested online: http://nlp.stanford.edu:8080/parser/index.jsp

This answer was tested with Stanford CoreNLP 3.6.0., pycorenlp 0.3.0 and python 3.5 x64 on Windows 7 SP1 x64 Ultimate.