How to use "Universal dependencies, enhanced" parser from stanford NLP in nltk?

My way is to Bypass the interface NLTK provided and directly look at their implementation.

Find the source code ./nltk/parse/corenlp.py.

In GenericCoreNLPParser class, there is a method called api_call. when you construct your dependency parser object. you could call this method to get a raw JSON object.

You will get a JSON object with keys: basic dependencies, enhancedDependencies, enhancedPlusPlusDependencies, tokens. When getting the result. we could write a simple function to parse the result into the format which is same as calling their interface.

Here are some snippets

    def parse_sentence(self, sentence: str) -> object:
        """
        Parse a sentence for given sentence and with user-defined properties
        :type properties: object
        :type sentence: str
        The pizza is overpriced
        return : Json Object from the NLP server.
        """
        return self.get_parser().api_call(sentence)["sentences"][0]

Once you get the result.

    def create_parsing_tuples(self, response: object, parse_type: str) -> list:
        """
        According to raw parse result, create dependency tuples by parse_type.
        parse_type options: basicDependencies , enhancedDependencies, enhancedPlusPlusDependencies

        :param response:
        :param parse_type:
        :return:
        """

        tuple_list = []
        for dependency in response[parse_type]:
            if dependency['dep'] == 'ROOT':
                continue
            governor = (dependency['governorGloss'], response['tokens'][dependency['governor'] - 1]['pos'])
            relation = dependency['dep']
            dependent = (dependency['dependentGloss'], response['tokens'][dependency['dependent'] - 1]['pos'])
            tuple_list.append((governor, relation, dependent))

        return [tuple_list]

In their source code, they are going to transform the JSON object to a tree structure, which is more generic in most cases.

Here is a demo picture

Wish my post would help somehow.