The first step: cleaning text and breaking it into tokens.
Before a computer can process human language, the raw text must be converted into a structured, numerical format. This crucial first step is known as text preprocessing and tokenization. Preprocessing involves a series of cleaning and normalization steps to reduce the complexity and noise in the text data. Common preprocessing tasks include converting all text to a single case (usually lowercase) to treat 'The' and 'the' as the same word, removing punctuation marks which often don't carry semantic meaning, and stripping out HTML tags or other irrelevant metadata. Another common step is 'stop word' removal, where extremely common words like 'a', 'an', 'the', 'is' are filtered out, as they may not be useful for certain tasks like topic modeling. After cleaning, the text is tokenized. Tokenization is the process of breaking down a stream of text into smaller units called tokens. These tokens are the fundamental building blocks that a model will work with. The simplest method is word tokenization, where the text is split by spaces and punctuation. For example, the sentence 'AI is powerful!' might be tokenized into ['ai', 'is', 'powerful']. While simple, this approach can struggle with languages without clear word boundaries and can lead to very large vocabularies, causing issues with out-of-vocabulary words. More advanced techniques, like subword tokenization (which we will cover later), address these issues by breaking words down into smaller, meaningful parts. The result of this entire process is a structured representation of the text, typically a list of tokens, which can then be converted into numerical vectors for input into a machine learning model.