Sunday 9 a.m.–12:20 p.m.
Natural Language Processing with NLTK and Gensim
- Audience level:
- Python Libraries
In this tutorial, we will begin by exploring the features of the NLTK library. We will then focus on building a language-aware data product - a topic identification and document clustering algorithm from a web crawl of blog sites. The clustering algorithm will use a simple Lesk K-Means clustering to start, and then will improve with an LDA analysis using the popular Gensim library.
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the natural language world - unstructured data that by its very nature has latent information that is important to humans. NLP practitioners have benefited from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, Gensim, and the Natural Language Toolkit (NLTK). NLTK is an excellent library for machine-learning based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications. In this tutorial we will begin by exploring NLTK from the view of the corpora that it already comes with, and in this way we will get a feel for the various features and functionality that NLTK has. However, most NLP practitioners want to work on their own corpora, therefore during the second half of the tutorial we will focus on building a language aware data product from a specific corpus - a topic identification and document clustering algorithm from a web crawl of blog sites. The clustering algorithm will use a simple Lesk K-Means clustering to start, and then will improve with an LDA analysis using the Gensim library.