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NLTK

Language Models

Language models

If you come from a statistical background or a machine learning one then probably you don’t need any reasons for why it’s useful to build language models. If not, here’s what language models are and why they are useful. What is a model? Generally speaking, a model (in the statistical sense of course) is a […]

Introduction to Python NLTK

Introduction to NLTK

NLTK (Natural Language ToolKit) is the most popular Python framework for working with human language. There’s a bit of controversy around the question whether NLTK is appropriate or not for production environments. Here’s my take on the matter: NLTK doesn’t come with super powerful trained models (like other frameworks do, like Stanford CoreNLP) NLTK is […]

Splitting text into sentences

Splitting text into sentences

Few people realise how tricky splitting text into sentences can be. Most of the NLP frameworks out there already have English models created for this task. You might encounter issues with the pretrained models if: You are working with a specific genre of text(usually technical) that contains strange abbreviations. You are working with a language […]

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 […]

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:

In other words, in a shallow parse tree, there’s one maximum level between the […]

stemming

Stemmers vs. Lemmatizers

Stackoverflow is full of questions about why stemmers and lemmatizers don’t work as expected. The root cause of the confusion is that their role is often misunderstood. Here’s a comparison: Both stemmers and lemmatizers try to bring inflected words to the same form Stemmers use an algorithmic approach of removing prefixes and suffixes. The result […]

Building a simple inverted index using NLTK

In this example I want to show how to use some of the tools packed in NLTK to build something pretty awesome. Inverted indexes are a very powerful tool and is one of the building blocks of modern day search engines. While building the inverted index, you’ll learn to: 1. Use a stemmer from NLTK […]

NLP-FOR-HACKERS