What Is NLP or Natural Language Processing?

NLP is a field of Artificial Intelligence that helps computers understand and interpret human language. It’s like teaching a computer to speak and understand languages just like humans do.


h. rose

3/26/20234 min read

black iphone 5 on black and silver speaker siri alexa
black iphone 5 on black and silver speaker siri alexa

How does Siri or Alexa understand my commands so well? 

They use a technology called Natural Language Processing (NLP)! NLP is a field of Artificial Intelligence that helps computers understand and interpret human language. It’s like teaching a computer to speak and understand languages just like humans do.

NLP is an incredibly important part of Artificial Intelligence. It helps computers interact with people in a more natural and human-like way. NLP makes it possible for us to use voice commands to control devices and ask questions, like asking Siri for the weather or Alexa to play a song. Without NLP, we would have to use a more structured and formal language for our computers to understand us.

NLP is making a huge impact on modern society, from virtual assistants like Siri and Alexa to customer service chatbots. With NLP, computers can understand and respond to our questions and needs in a way that feels more human. This makes our lives easier and more convenient, and it allows us to interact with technology in a more natural and intuitive way. Plus, as NLP continues to advance, it has the potential to transform industries like healthcare, education, and more.

How Does NLP Work?

NLP is a fascinating field within Artificial Intelligence (AI) that focuses on teaching computers to understand and interpret human language.

To do this, NLP involves a process of collecting large amounts of data, parsing that data into meaningful pieces, and analyzing it to identify patterns and structures. This allows computers to recognize the context and meaning of human language, and respond in a way that makes sense to us.

There are a variety of techniques used in NLP, such as machine learning and deep learning, which allow computers to continually improve their ability to understand and respond to human language. These techniques help computers to recognize patterns and make predictions based on what they've learned from previous interactions with humans.

Real-world examples of NLP in action are all around us. Have you ever used a chatbot to get customer service help online? That's an example of NLP in action. Or have you ever used language translation software to talk to someone who speaks a different language? That's another example of NLP in action. As our world becomes more global and interconnected, NLP is becoming increasingly important for helping us communicate with one another in a way that feels natural and intuitive.

NLP In Society

Let's take a closer look at the different ways NLP is being used in society!

First up, healthcare! With the help of NLP, doctors and researchers are able to analyze vast amounts of medical data, including patient records and scientific studies, to develop new treatments and improve patient outcomes.

But that's not all! NLP is also being used in finance to analyze financial data and make better investment decisions. By processing large amounts of information, NLP algorithms can identify patterns and predict market trends.

And have you ever used a chatbot to get help with a customer service issue? That's thanks to NLP too! Companies use NLP to train chatbots to understand and respond to customer inquiries, providing faster and more efficient customer service.

Finally, in the media and entertainment industry, NLP is being used to analyze sentiment in social media and other sources. This helps companies understand what people are saying about their brand and products, and adjust their marketing strategies accordingly.

The Future Of NLP In AI

Exciting advancements are coming that will make NLP even more amazing than it already is! With advancements in machine learning and deep learning, NLP will become even more accurate and powerful. This means we can look forward to even more amazing applications that will make our lives easier and better.

One exciting potential application of NLP is in personalized marketing. Imagine ads that are tailored specifically to you and your interests! NLP can analyze your browsing history, search terms, and other data to create personalized ads that are more relevant to you. This means you'll see fewer ads that don't interest you and more ads that actually help you find what you're looking for.

Education is another area where NLP is expected to have a big impact. By analyzing student data, NLP can help teachers personalize learning and improve student outcomes. With NLP, teachers can identify struggling students earlier and provide targeted support to help them succeed. Plus, NLP can help students learn more efficiently and effectively by creating personalized learning plans tailored to their unique needs.

Of course, there are still challenges facing the future of NLP. One of the biggest challenges is the need for more data. NLP requires vast amounts of data to be trained and optimized, and without enough data, NLP won't be as effective. Additionally, there is the potential for bias in NLP algorithms. As we rely more on NLP to make decisions, it's important that we ensure these algorithms are fair and unbiased. But with these challenges in mind, we can work to make NLP even better and ensure it continues to make our lives easier and more efficient.

Additional Resources:


  1. Jurafsky, D., & Martin, J. H. (2020). Speech and language processing. Pearson.

  2. Goldberg, Y. (2017). Neural network methods for natural language processing. Morgan & Claypool Publishers.

  3. Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. MIT press.


  1. Chen, M., & Skiena, S. (2014). Building sentiment lexicons for all major languages. Proceedings of the eighth international AAAI conference on weblogs and social media.

  2. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12(Aug), 2493-2537.

  3. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

  4. Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. URL https://s3-us-west-2.amazonaws.com/openai-assets/researchcovers/languageunsupervised/language_understanding_paper.pdf


  1. Natural Language Processing with Python - offered by University of Michigan on Coursera

  2. CS224n: Natural Language Processing with Deep Learning - offered by Stanford University


  1. NLTK (Natural Language Toolkit)

  2. spaCy

  3. Gensim