How to make a tree from the output of a dependency parser?

问题

I am trying to make a tree (nested dictionary) from the output of dependency parser. The sentence is "I shot an elephant in my sleep". I am able to get the output as described on the link: How do I do dependency parsing in NLTK?

nsubj(shot-2, I-1)
det(elephant-4, an-3)
dobj(shot-2, elephant-4)
prep(shot-2, in-5)
poss(sleep-7, my-6)
pobj(in-5, sleep-7)

To convert this list of tuples into nested dictionary, I used the following link: How to convert python list of tuples into tree?

def build_tree(list_of_tuples):
    all_nodes = {n[2]:((n[0], n[1]),{}) for n in list_of_tuples}
    root = {}    
    print all_nodes
    for item in list_of_tuples:
        rel, gov,dep = item
        if gov is not 'ROOT':
            all_nodes[gov][1][dep] = all_nodes[dep]
        else:
            root[dep] = all_nodes[dep]
    return root

This gives the output as follows:

{'shot': (('ROOT', 'ROOT'),
  {'I': (('nsubj', 'shot'), {}),
   'elephant': (('dobj', 'shot'), {'an': (('det', 'elephant'), {})}),
   'sleep': (('nmod', 'shot'),
    {'in': (('case', 'sleep'), {}), 'my': (('nmod:poss', 'sleep'), {})})})}

To find the root to leaf path, I used the following link: Return root to specific leaf from a nested dictionary tree

[Making the tree and finding the path are two separate things]The second objective is to find the root to leaf node path like done Return root to specific leaf from a nested dictionary tree. But I want to get the root-to-leaf (dependency relationship path) So, for instance, when I will call recurse_category(categories, 'an') where categories is the nested tree structure and 'an' is the word in the tree, I should get ROOT-nsubj-dobj (dependency relationship till root) as output.

回答1:

This converts the output to the nested dictionary form. I will keep you updated if I can find the path as well. Maybe this, is helpful.

list_of_tuples = [('ROOT','ROOT', 'shot'),('nsubj','shot', 'I'),('det','elephant', 'an'),('dobj','shot', 'elephant'),('case','sleep', 'in'),('nmod:poss','sleep', 'my'),('nmod','shot', 'sleep')]

nodes={}

for i in list_of_tuples:
    rel,parent,child=i
    nodes[child]={'Name':child,'Relationship':rel}

forest=[]

for i in list_of_tuples:
    rel,parent,child=i
    node=nodes[child]

    if parent=='ROOT':# this should be the Root Node
            forest.append(node)
    else:
        parent=nodes[parent]
        if not 'children' in parent:
            parent['children']=[]
        children=parent['children']
        children.append(node)

print forest

The output is a nested dictionary,

[{'Name': 'shot', 'Relationship': 'ROOT', 'children': [{'Name': 'I', 'Relationship': 'nsubj'}, {'Name': 'elephant', 'Relationship': 'dobj', 'children': [{'Name': 'an', 'Relationship': 'det'}]}, {'Name': 'sleep', 'Relationship': 'nmod', 'children': [{'Name': 'in', 'Relationship': 'case'}, {'Name': 'my', 'Relationship': 'nmod:poss'}]}]}]

The following function can help you to find the root-to-leaf path:

def recurse_category(categories,to_find):
    for category in categories: 
        if category['Name'] == to_find:
            return True, [category['Relationship']]
        if 'children' in category:
            found, path = recurse_category(category['children'], to_find)
            if found:
                return True, [category['Relationship']] + path
    return False, []

回答2:

Firstly, if you're just using the pre-trained model for the Stanford CoreNLP dependency parser, you should use the CoreNLPDependencyParser from nltk.parse.corenlp and avoid using the old nltk.parse.stanford interface.

See Stanford Parser and NLTK

After downloading and running the Java server in terminal, in Python:

>>> from nltk.parse.corenlp import CoreNLPDependencyParser
>>> dep_parser = CoreNLPDependencyParser(url='http://localhost:9000')
>>> sent = "I shot an elephant with a banana .".split()
>>> parses = list(dep_parser.parse(sent))
>>> type(parses[0])
<class 'nltk.parse.dependencygraph.DependencyGraph'>

Now we see that the parses are of type DependencyGraph from nltk.parse.dependencygraph https://github.com/nltk/nltk/blob/develop/nltk/parse/dependencygraph.py#L36

To convert the DependencyGraph to a nltk.tree.Tree object by simply doing DependencyGraph.tree():

>>> parses[0].tree()
Tree('shot', ['I', Tree('elephant', ['an']), Tree('banana', ['with', 'a']), '.'])

>>> parses[0].tree().pretty_print()
          shot                  
  _________|____________         
 |   |  elephant      banana    
 |   |     |       _____|_____   
 I   .     an    with         a

To convert it into the bracketed parse format:

>>> print(parses[0].tree())
(shot I (elephant an) (banana with a) .)

If you're looking for dependency triplets:

>>> [(governor, dep, dependent) for governor, dep, dependent in parses[0].triples()]
[(('shot', 'VBD'), 'nsubj', ('I', 'PRP')), (('shot', 'VBD'), 'dobj', ('elephant', 'NN')), (('elephant', 'NN'), 'det', ('an', 'DT')), (('shot', 'VBD'), 'nmod', ('banana', 'NN')), (('banana', 'NN'), 'case', ('with', 'IN')), (('banana', 'NN'), 'det', ('a', 'DT')), (('shot', 'VBD'), 'punct', ('.', '.'))]

>>> for governor, dep, dependent in parses[0].triples():
...     print(governor, dep, dependent)
... 
('shot', 'VBD') nsubj ('I', 'PRP')
('shot', 'VBD') dobj ('elephant', 'NN')
('elephant', 'NN') det ('an', 'DT')
('shot', 'VBD') nmod ('banana', 'NN')
('banana', 'NN') case ('with', 'IN')
('banana', 'NN') det ('a', 'DT')
('shot', 'VBD') punct ('.', '.')

In CONLL format:

>>> print(parses[0].to_conll(style=10))
1   I   I   PRP PRP _   2   nsubj   _   _
2   shot    shoot   VBD VBD _   0   ROOT    _   _
3   an  a   DT  DT  _   4   det _   _
4   elephant    elephant    NN  NN  _   2   dobj    _   _
5   with    with    IN  IN  _   7   case    _   _
6   a   a   DT  DT  _   7   det _   _
7   banana  banana  NN  NN  _   2   nmod    _   _
8   .   .   .   .   _   2   punct   _   _

来源：https://stackoverflow.com/questions/52148690/how-to-make-a-tree-from-the-output-of-a-dependency-parser

标签

python

dictionary

nlp

nltk

stanford-nlp