Containerized CoreNLP
ccNLP serves up Brendan O'Connor's Stanford CoreNLP wrapper as an API. Its a wrapper for a wrapper...
There are probably a number of ways this can be done much better, particularly by someone with some Java skills. However I have yet to come across a fast implementation of Stanford CoreNLP as a RESTful API, so in the meantime this will do. Pull requests welcome.
ccNLP runs as a Flask-RESTful service on port 5000. You can can configure the
parser just as you would with the normal wrapper in
resources/stanford_config.ini. Set the path to the jars in
resources/config.ini.
Its also Dockerized, which will hopefully make it easier to run, but it should also work natively.
- Make sure you have docker installed
- Build the image:
sudo docker build -t ccnlp . - Startup a container:
sudo docker run -d -p 5000:5000 ccnlp
You can send the API large chunks of text, but this can be slow and may throw an error on the wrapper side. I recommend going by sentence or chuncks in that range.
###Python
import json
import requests
headers = {'Content-Type': 'application/json'}
data = {'text': 'Stanford CoreNLP provides a set of natural language analysis tools which can take raw text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, and mark up the structure of sentences in terms of phrases and word dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, etc.'}
data = json.dumps(data)
r = requests.post('http://localhost:5000/process', data=data, headers=headers)
r.json()