Natural Language Processing Essentials: A Simple Introduction and Some Key Insights From a Dev

Someone once told me, If you want to learn about something, start teaching it to others. While I am not good at 1:1 teaching or creating YouTube videos, I chose to blog as my medium of teaching.

Around the start of the year 2022, I made a switch from a full-time front-end engineer to a Natural Language Processing (NLP) Engineer in my current organization. I started learning more about the NLP technologies that were being widely used. I got introduced to Stanford NLP, BERT, and related technologies.

While I was getting comfortable with all of these, Chatgpt started to gain traction, and everyone was talking about it, and that is when I got introduced to terms like transformer learning, self-supervised learning, etc. It was when I decided to re-learn the concepts of Natural Language Processing (NLP) that I started this series.

I will start with the basics of NLP and take you through my learnings along the way in the upcoming months. So let's get going.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that focuses on the interaction between computers and human language. It is an interdisciplinary field that combines linguistics, computer science, and information engineering to understand, interpret, and generate human language.

Why use NLP

One of the main goals of NLP is to enable computers to understand and analyze human language in a way that is similar to how humans do it. This allows computers to perform tasks such as language translation, text summarization, sentiment analysis, and more.

Some of the most common NLP techniques include:

• Tokenization: breaking down the text into individual words or phrases
• Lemmatization: reducing words to their base form
• Part-of-speech tagging: identifying the role of each word in a sentence (e.g. noun, verb, adjective)
• Named Entity Recognition: identifying and classifying proper nouns in the text (e.g. people, places, organizations)
• Sentiment Analysis: determining the emotional tone of text (e.g. positive, negative, neutral)

Usage of NLP

NLP is used in a wide range of applications, such as chatbots, virtual assistants, language translation, and text summarization. In recent years, NLP has become increasingly important in industries such as finance, healthcare, and customer service.

NLP is a rapidly growing field, and new techniques and technologies are constantly being developed to improve the accuracy and functionality of NLP systems. With the increasing amount of data being generated and the growing importance of understanding and analyzing human language, NLP is set to play an increasingly important role in the future of technology.

In conclusion, NLP is a powerful tool that can be used to analyze and understand human language. From chatbots to text summarization, NLP is being used in a wide range of applications and industries. With the rapid pace of development in the field, it is an exciting time for NLP and its potential to revolutionize the way we interact with technology.

That's all for now, folks. In the next blog in this series, I will cover more details on the NLP techniques I have listed above.

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