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Natural Language Processing - Introduction

What is Natural Language Processing?

This is probably the first post I should have written on the blog. The thing is, I did machine learning and natural language processing for a long time before putting the concepts in order inside my own mind.

I’ve learned techniques and hacks to boost precision of classifiers before fully understanding how a classifier computes its weights or whatever. So I guess it makes sense to publish a general introductory post after some real hands-on posts.

Here’s a popular diagram used to describe what data science usually implies:
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Training a NER System Using a Large Dataset

In a previous article, we studied training a NER (Named-Entity-Recognition) system from the ground up, using the Groningen Meaning Bank Corpus. This article is a continuation of that tutorial. The main purpose of this extension to training a NER is to:

  1. Replace the classifier with a Scikit-Learn Classifier
  2. Train a NER on a larger subset of the training data
  3. Increase accuracy
  4. Understand Out Of Core Learning

What was wrong with the initial system you might ask. There wasn’t anything fundamentally wrong with the process. In fact, it’s a great didactical example, and we can build upon it. This is where it was lacking:
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natural language processing pipeline

Building a NLP pipeline in NLTK

If you have been working with NLTK for some time now, you probably find the task of preprocessing the text a bit cumbersome. In this post, I will walk you through a simple and fun approach for performing repetitive tasks using coroutines. The coroutines concept is a pretty obscure one but very useful indeed. You can check out this awesome presentation by David Beazley to grasp all the stuff needed to get you through this (plus much, much more).
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text chunking

Text Chunking with NLTK

What is chunking

Text chunking, also referred to as shallow parsing, is a task that follows Part-Of-Speech Tagging and that adds more structure to the sentence. The result is a grouping of the words in “chunks”. Here’s a quick example:
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