How to Learn Natural Language Processing

Key takeaways

Natural language processing (NLP) is an artificial intelligence (AI) technology powering everyday applications and devices such as search engines and chatbots. Here are some important things to know:

• NLP is big business, and it’s growing: Experts expect the NLP market to be worth $158.04 billion by 2032 [1]. 

• NLP has found its way into a variety of industries and sectors, with many benefits for individuals and companies alike. 

• You’ll need to understand how NLP models learn and become familiar with different approaches to NLP programming.

Explore the practical applications of NLP, the required background knowledge, and how you can start learning and implementing NLP into your personal or professional life. Then, consider enrolling in the IBM AI Engineering Professional Certificate, where you’ll build deep learning models and neural networks using Keras, PyTorch, and TensorFlow. 

Why learn natural language processing?

NLP is a subfield of artificial intelligence that uses machine learning (ML) and other technologies and disciplines to train computer models to understand and use human language. NLP is key to how today’s AI interfaces learn from large language models (LLMs), the frameworks responsible for powering increasingly advanced generative AI technology. 

Generally speaking, NLP is a combination of the following disciplines: 

• Machine learning

• Deep learning

• Statistical modeling

• Computational linguistics

• Philosophy

• Computer science

• Cognitive science

• Mathematics

You likely come across NLP every day since it powers often-used devices and applications such as: 

• Automated summary tools

• Chatbots

• Digital assistants (e.g., Amazon’s Alexa, Microsoft’s Cortana, etc.)

• Search engines

• Voice-operated GPS 

NLP is big business, and it’s growing: Experts expect the NLP market to be worth $158.04 billion by 2032 [1]. Now may be the right time to learn more about this exciting new technology.

Key concepts in learning NLP

To grasp NLP, you’ll need to understand key concepts, such as how NLP models learn: 

• Coreference resolution: Recognizing that two words in a text refer to the same entity—for example, when a pronoun refers to an object or person 

• Lemmatization: Breaking words with related meanings down to their common root—for example, breaking simply and simplification down to the shared root simple 

• Named entity recognition: Identifying and sorting related words, values, or symbols into categories such as names, locations, and percentages 

• Part-of-speech tagging: Identifying and sorting words in a sentence by what part of speech they are, such as nouns, verbs, or adjectives. 

• Stop word removal: Extracting relatively insignificant words—often prepositions—from sentences during the training process 

• Tokenization: Breaking down a sentence into individual parts or even into individual words or phrases

• Word sense disambiguation: Identifying which meaning applies to a word with multiple meanings based on semantic analysis 

You’ll also need to grasp several different approaches to NLP programming. These include: 

• Supervised NLP: Via labeled inputs and outputs, an NLP program learns to classify data and offer subsequent output more accurately over time. 

• Unsupervised NLP: An NLP program classifies data via an ML algorithm. It learns, over time, to sort unlabeled data sets accurately by discovering patterns in them.

• Natural language generation (NLG): By focusing on keywords and topics, an NLP learns to produce human-like conversation relevant to a specific topic. 

• Natural language understanding (NLU): An NLP analyzes the contextual or semantic meaning of sentences rather than just classifying the individual words in them. 

Deep learning for NLP

Another cornerstone of NLP is deep learning. Deep learning modalities allow NLP programs to analyze enormous stores of unstructured data more accurately than ever before. As such, programmers often favor deep learning when developing their NLP programs. 

Deep learning aims to train AI models, including those utilizing NLP, to learn from the input they receive. Deep learning trains these models to extract patterns from input data, from which the AI model generates accurate output concerning its antecedent input data. 

Deep learning methodologies vary. Modern programmers often use the transformer model. Transformers can parse larger data sets more quickly than previous ML models because they process inputs in parallel rather than sequentially, word by word. This slowed down previous deep learning frameworks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). 

One of the most popular transformer models is ChatGPT (GPT stands for generative pre-trained transformer). Another popular transformer model is the bidirectional encoder representations from transformers (BERT) model, which is the basis of Google’s search engine algorithm. 

Prerequisites and background knowledge

To learn NLP systematically, you’ll need to have knowledge of several programming languages. Python remains the most popular. Other common programming languages NLP learners might acquire include: 

• Java

• JavaScript

• Julia

• R

You’ll also need knowledge of common NLP libraries and frameworks, such as: 

• Gensim

• Hugging Face

• Natural Language Toolkit (NLTK)

• PyTorch

• spaCy

• TensorFlow

• TidyText

• Weka

• WordNet

And because NLP operates based on advanced probability algorithms, a background in statistical analysis and familiarity with other ML techniques will help you grasp the essentials of NLP more easily. 

How to get started learning NLP

You can get started learning NLP right away with these tips: 

• Learn the basics. Get started by understanding the fundamental concepts of NLP—what it is, what it’s good for, and how you want to use it. 

• Learn Python. Python remains the most common NLP programming language, so start there. You may also want to learn other programming languages like R and Java. 

• Familiarize yourself with NLP libraries. TensorFlow, spaCy, and NLTK are popular NLP library options. You can learn to work with NLTK, which is free and open-source, on its website. 

• Develop your own project. Some people find the best way to learn is to be very hands-on. Work on a small NLP project of your own to see how the concepts and skills you’ve learned come together holistically.

• Explore online communities for learners. Seek out other NLP learners online and learn together. Ask more advanced learners about their research and ask them to field any questions you may have. Share your work and request feedback. 

• Look into online courses. You can learn about NLP on Coursera. 

Learn natural language processing with Coursera

NLP is an exciting subfield of AI offering extensive technological, social, and professional development opportunities. Get started today by familiarizing yourself with key terms in our AI glossary, or explore these free resources:

• Watch on YouTube: How Does GenAI Work?

• Hear from an expert: Becoming an AI Engineer: 7 Questions with an IBMer

• Get our insights: Subscribe to our Career Chat newsletter

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