7

I am working on a problem to represent knowledge extracted from a paragraph and rank it to produce abstractive summaries. I have implemented dependency parsing using Stanford NLP, which gives dot format graph as an output.

The dependency-parsed output of the following two sentences are as follows.

Sentence 1 - John is a computer scientist

Dot format - 

digraph G{
edge [dir=forward]
node [shape=plaintext]

0 [label="0 (None)"]
0 -> 5 [label="root"]
1 [label="1 (John)"]
2 [label="2 (is)"]
3 [label="3 (a)"]
4 [label="4 (computer)"]
5 [label="5 (scientist)"]
5 -> 2 [label="cop"]
5 -> 4 [label="compound"]
5 -> 3 [label="det"]
5 -> 1 [label="nsubj"]
}

Graph - enter image description here

Sentence 2 - John has an elder sister named Mary.

Dot Format - 

digraph G{
edge [dir=forward]
node [shape=plaintext]

0 [label="0 (None)"]
0 -> 2 [label="root"]
1 [label="1 (John)"]
2 [label="2 (has)"]
2 -> 5 [label="dobj"]
2 -> 1 [label="nsubj"]
3 [label="3 (an)"]
4 [label="4 (elder)"]
5 [label="5 (sister)"]
5 -> 6 [label="acl"]
5 -> 3 [label="det"]
5 -> 4 [label="amod"]
6 [label="6 (named)"]
6 -> 7 [label="dobj"]
7 [label="7 (Mary)"]
}

Graph - enter image description here

Now I want to merge this graph at a common node, John. I am currently using graphviz to import dot graph like this,

from graphviz import Source
s = Source(dotGraph, filename=filepath, format="png")

But there seems to be no functionality to merge graphs in Graphviz, or Networkx. So how can this be done?

Improve this question
4
  • 1
    Commissioner Gordon, turn on the Merge Signal. This is a case for the Biosyntax Squad.
    – jlawler
    Commented Feb 4, 2017 at 15:51
  • What would your expected output look like? How exactly does that help you achieve your stated purpose?
    – Lefty G Balogh
    Commented Feb 5, 2017 at 13:38
  • The goal is to have all the information related to a particular entity, in the same graph. Further, it can be ranked and used to generate summaries.
    – Riken Shah
    Commented Feb 6, 2017 at 6:21
  • I am a theoretical DG guy. I cannot comment on the computational side of what you are doing. I would, though, like to point out that the Stanford annotation scheme is controversial. For instance, your first dependency tree shows scientist as the root of the sentence. From a linguistic point of view, a stronger case can be made for viewing the finite verb is as the root.
    – Tim Osborne
    Commented Apr 12, 2020 at 15:14

1 Answer 1

Reset to default
1

Since you are using CoreNLP to generate dependency trees, a very nice way to tackle your problem would be to use the Tsurgeon library used to manipulate parse trees.

Tsurgeon is a (parse) tree transformation language. (Also check Tregex and SemGrex on the same link.)

Improve this answer

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.