Getting started with NLP

Many of you have been asking me how to get started with NLP and what I would do if I wanted to get started.

Today I want to talk about how easy it is to get started with language-based applications by utilizing the NLTK Python library.

What is NLP and NLTK?

Natural Language Processing (NLP) is a subfield of AI that primarily focuses on giving computers the ability to understand and generate meaningful human language.

Natural Language Toolkit (NLTK), which is the most popular and easiest Python library for NLP with a substantial community behind it.

Getting started with NLP

Install NLTK using pip and download Punkt with NLTK (refer to the image below).

5 Basic NLTK Functions

1. Tokenization
   
   Tokenization is the process of breaking chunks of text down into words or sentences.
   
   You are essentially creating “tokens“ (building blocks), which is important as the most common way of processing text happens at the token level.
   

2. Stopword Removal
   
   Stopwords are commonly used words such as “the“ and “in“ that are removed during NLP preprocessing for better analysis.

   

3. Part-of-Speech Tagging (POS)
   
   Part-of-speech tagging is the process of labeling every word in a sentence with its corresponding part of speech such as a noun and verb.
   
   You can do this by using the pos_tag() function.

   

4. Named Entity Recognition (NER)
   
   Named Entity Recognition is a process used to classify named entities in text such as names, and dates. and locations.
   
   This can be accomplished with the ne_chunk() function.

   

5. Concordance and Similarity
   
   NLTK will allow you to explore the similarity and word usage in a mass collection of text.
   
   You can locate certain words or find words in similar contexts within any text.

This guide provides a foundational understanding of NLP using Python and NLTK.

From here, you can explore deeper into the world of NLP using Python and NLTK to start your own language-based applications!

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