Python for NLP: Getting Started with the StanfordCoreNLP Library
This is the ninth article in my series of articles on Python for NLP. In the previous article, we saw how Python’s Pattern library can be used to perform a variety of NLP tasks ranging from tokenization to POS tagging, and text classification to sentiment analysis. Before that we explored the TextBlob library for performing similar natural language processing tasks.
In this article, we will explore StanfordCoreNLP library which is another extremely handy library for natural language processing. We will see different features of StanfordCoreNLP with the help of examples. So before wasting any further time, let’s get started.
Setting up the Environment
The installation process for StanfordCoreNLP is not as straight forward as the other Python libraries. As a matter of fact, StanfordCoreNLP is a library that’s actually written in Java. Therefore make sure you have Java installed on your system. You can download the latest version of Java freely.
Once you have Java installed, you need to download the JAR files for the StanfordCoreNLP libraries. The JAR file contains models that are used to perform different NLP tasks. To download the JAR files for the English