Introduction to natural language processing
1. Introduction to natural language processing
Hello and welcome to this course on natural language processing, or NLP!2. Meet the instructor...
My name is Fouad, and I'm a machine learning engineer and research scientist. My work focuses on using NLP to solve real-world challenges, especially in cybersecurity and healthcare. In this course, we'll learn to teach computers to understand and process human language.3. What is NLP?
Language is all around us; it's in books, websites, social media posts, and emails. It's the primary way we communicate. However, computers don't naturally understand language the way humans do. So, how can we teach a computer to "read" and "understand" text?4. What is NLP?
That's where NLP, comes in. Think of NLP as a translator: it converts human language into a format that machines can understand and process. This enables computers to analyze and make sense of human language.5. NLP workflow
To convert and process human language, we move through an NLP workflow that starts with raw text, which could be anything from a tweet to a paragraph in a book.6. NLP workflow
Next is preprocessing, where we clean the text and remove unnecessary elements.7. NLP workflow
Then, we extract features by converting the cleaned text into numbers, something machines can understand.8. NLP workflow
These numerical representations are then fed into a model, allowing the machine to analyze the text, make predictions, classify information, or even generate new content, which becomes the final output.9. Course plan
In this course, we'll follow this workflow step by step. Chapter 1 will focus on text processing, using the natural language processing toolkit, or NLTK.10. Course plan
Chapter 2 will cover feature extraction using Scikit learn and Gensim.11. Course plan
Chapters 3 and 4 introduce pre-trained pipelines from Hugging Face's Transformers library, which combine all three steps and enable us to seamlessly apply NLP techniques across various applications.12. Tokenization
Tokenization is the first step in preprocessing. It breaks a large chunk of text into tokens, which represent smaller, manageable pieces. Think of it like chopping vegetables. Instead of cooking a whole carrot, we slice it into smaller pieces to make it easier to cook.13. Sentence tokenization
Sentence tokenization breaks text into individual sentences. This can offer clearer insights than analyzing the text as a whole. In translation, for example, sentence tokenization helps models translate more accurately since sentences are structured differently in different languages. To implement this in code, we import the nltk library, download the punkt_tab resource, which helps NLTK recognize sentence boundaries, define the text to be split, and split it with nltk.sent_tokenize(). The result is a list of sentences that we can analyze individually.14. Word tokenization
Word tokenization splits sentences into individual words and punctuation, useful for tasks like identifying keywords or counting word frequency. For example, identifying spam emails relies on identifying spam-triggering terms. Given a text, we use nltk.word_tokenize(), which produces a list of tokens for further processing.15. Let's practice!
Before exploring what to do with tokenized text, let's take a moment to practice.Create Your Free Account
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