Dependency parsing (DP) is a modern parsing mechanism. The main concept of DP is that each linguistic unit (words) is connected with each other by a directed link. These links are called dependencies in linguistics. There is a lot of work going on in the current parsing community. While phrase structure parsing is still widely used for free word order languages (Czech and Turkish), dependency parsing has turned out to be more efficient.

A very clear distinction can be made by looking at the parse tree generated by phrase structure grammar and dependency grammar for a given example, as the sentence "The big dog chased the cat". The parse tree for the preceding sentence is:

If we look at both parse trees, the phrase structures try to capture the relationship between words and phrases and then eventually between phrases. While a dependency tree just looks for a dependency between words, for example, big is totally dependent on dog.

NLTK provides a couple of ways to do dependency parsing. One of them is to use a probabilistic, projective dependency parser, but it has the restriction of training with a limited set of training data. One of the state of the art dependency parsers is a Stanford parser. Fortunately, NLTK has a wrapper around it and in the following example, I will talk about how to use a Stanford parser with NLTK:

# Stanford Parser [Very useful]
>>>from nltk.parse.stanford import StanfordParser
>>>english_parser = StanfordParser('stanford-parser.jar', 'stanford-parser-3.4-models.jar')
>>>english_parser.raw_parse_sents(("this is the english parser test")
Parse
(ROOT
  (S
    (NP (DT this))
    (VP (VBZ is)
      (NP (DT the) (JJ english) (NN parser) (NN test)))))
Universal dependencies
nsubj(test-6, this-1)
cop(test-6, is-2)
det(test-6, the-3)
amod(test-6, english-4)
compound(test-6, parser-5)
root(ROOT-0, test-6)
Universal dependencies, enhanced
nsubj(test-6, this-1)
cop(test-6, is-2)
det(test-6, the-3)
amod(test-6, english-4)
compound(test-6, parser-5)
root(ROOT-0, test-6)

The output looks quite complex but, in reality, it's not. The output is a list of three major outcomes, where the first is just the POS tags and the parsed tree of the given sentences. The same is plotted in a more elegant way in the following figure. The second is the dependency and positions of the given words. The third is the enhanced version of dependency: