Natural Language Processing (NLP) is a subset of machine learning in which we aim to train computers to understand human languages. The chatbot you see in a banking app, Siri on iPhone, or Google translator are examples of natural language processing. In the process of Natural Language Processing, we aim to prepare a textual dataset to build a vocabulary for text classification or clustering. If you want to understand the process of Natural Language Processing, this article is for you. In this article, I will walk you through the process of Natural Language Processing that you need to follow while working on any problem based on Natural Language Processing.
Process of Natural Language Processing
The complete process of natural language processing includes:
- Collecting textual data or documents
- Tokenization
- Stop words removal
- Stemming
- Vectorization
- Text classification or clustering
So this is the complete process or the pipeline that you have to follow while working on any problem based on Natural Language Processing. Now let’s go through this process step by step to understand the complete process of NLP.
Collecting Textual Data or Documents
In Natural Language Processing, we aim to train a computer to understand human languages. Every product made with NLP could not have been possible without the proper data. So, the very first step in the NLP process is to collect textual data or documents. A textual dataset is a data with textual features, and a textual document is like a vocabulary of words that you can use to build a model.
Tokenization
After collecting data, the next step is tokenization. Tokenization means splitting a piece of text into sentences or words. When we do tokenization on a text, it breaks the complete paragraph into sentences or sentences into words. You can learn more about tokenization and its implementation using Python from here.
Stop Words Removal
Stop words are a part of every language spoken by humans. (For example, is, the, are, a). We need to remove these words as these words do not carry much information in a textual dataset. You can learn more about stop words removal using Python from here.
Stemming
The next step in the process of Natural Language Processing is stemming. It means transforming all the verbs or plurals of a particular word into its radical form. Every search engine uses it to find the most helpful resource for a search query irrespective of the verbs or plurals used.
Vectorization
The next step in the process of Natural Language Processing is vectorization. It means to transform all the text tokens into numerical vectors. You cannot feed textual features directly into a machine learning model, so you have to convert them into numerical values, this is what vectorization means.
Text Classification or Clustering
After converting text data to numeric vectors, you get data with numerical values. You can use this dataset for text classification or clustering. Here is an example where you will learn the implementation of this complete process of NLP using Python for text classification.
Summary
So the complete process of NLP includes:
- Collecting textual data or documents
- Tokenization
- Stop words removal
- Stemming
- Vectorization
- Text classification or clustering
You can learn the implementation of this process using Python for text classification from here. I hope you liked this article on the process of NLP. Feel free to ask valuable questions in the comments section below.
