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The Python JSON-NLP package

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Python JSON-NLP Module

(C) 2019 by Damir Cavar, Oren Baldinger, Maanvitha Gongalla, Anurag Kumar, Murali Kammili

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The Python JSON-NLP module contains general mapping functions for JSON-NLP to CoNLL-U, a validator for the generated output, an Natural Language Processing (NLP) pipeline interface (for Flair, spaCy, NLTK, Polyglot, Xrenner, etc.), and various utility functions.


For more details, see JSON-NLP.

This module is a wrapper for outputs from different NLP pipelines and modules into a standardized JSON-NLP format.

To install this package, run the following command:

pip install pyjsonnlp

You might have to use pip3 on some systems.


JSON-NLP is based on a schema, built by, to comprehensively and concisely represent linguistic annotations. We provide a validator to help ensure that generated JSON validates against the schema:

result = MyPipeline().proces(text="I am a sentence")
assert pyjsonnlp.validation.is_valid(result)


To enable interoperability with other annotation formats, we support conversions between them. Note that conversion could be lossy, if the relative depths of annotation are not the same. Currently we have a CoNLL-U to JSON-NLP converter, that covers most annotations:


This functionality is still a work in progress.


JSON-NLP provides a simple Pipeline interface that should be implemented for embedding into a microservice:

from collections import OrderedDict

class MockPipeline(pyjsonnlp.pipeline.Pipeline):
    def process(text='', coreferences=False, constituents=False, dependencies=False, expressions=False,
                **kwargs) -> OrderedDict: 
        return OrderedDict()

The provided keyword arguments should be used to toggle on or off processing components within the method.


The next step is the JSON-NLP a Microservice class, with a pre-built implementation of [Flask].

from pyjsonnlp.microservices.flask_server import FlaskMicroservice

app = FlaskMicroservice(__name__, MyPipeline(), base_route='/')

We recommend creating a with the FlaskMicroservice class, which extends the [Flask] app. A corresponding WSGI file would contain:

from mypipeline.server import app as application

To disable a pipeline component (such as phrase structure parsing), add

application.constituents = False

The full list of properties available that can be disabled or enabled are

  • constituents
  • dependencies
  • coreference
  • expressions

The microservice exposes the following URIs:

  • /constituents
  • /dependencies
  • /coreference
  • /expressions
  • /token_list

These URIs are shortcuts to disable the other components of the parse. In all cases, tokenList will be included in the JSON-NLP output. An example url is:

http://localhost:5000/dependencies?text=I am a sentence

Text is provided to the microservice with the text parameter, via either GET or POST. If you pass url as a parameter, the microservice will scrape that url and process the text of the website.

Other parameters specific to your pipeline implementation can be passed as well:

http://localhost:5000?lang=en&constituents=0&text=I am a sentence.

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