Menu Sidebar

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:
Read More

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).
Read More

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:
Read More

Recipe: Text classification using NLTK and scikit-learn

Text classification is most probably, the most encountered Natural Language Processing task. It can be described as assigning texts to an appropriate bucket. A sports article should go in SPORT_NEWS, and a medical prescription should go in MEDICAL_PRESCRIPTIONS.

To train a text classifier, we need some annotated data. This training data can be obtained through several methods. Suppose you want to build a spam classifier. You would export the contents of your mailbox. You’d label the email in the inbox folder as NOT_SPAM and the contents of your spam folder as SPAM.
Read More

Newer Posts
Older Posts


Pin It on Pinterest