As we established, Recurrent Neural Networks, like most machine learning models, require numerical inputs. Raw text, with its strings of characters, words, and punctuation, is far from the structured tensors these networks expect. Therefore, a significant part of working with text involves transforming it into a suitable numerical format. This section provides a high-level overview of the common pipeline used to prepare text data for sequence models like LSTMs and GRUs.

The overall goal is to convert sequences of symbols (words, characters) into sequences of numbers, typically represented as multi-dimensional arrays or tensors. Think of it as translating human language into a machine-readable format while preserving the sequential nature of the original text. This process generally involves several stages:

Cleaning (Optional but Recommended): Real-world text often contains noise like HTML tags, special characters, or irrelevant punctuation. A preliminary cleaning step can remove or normalize these elements, simplifying subsequent processing.
Tokenization: This is the process of breaking down the raw text into smaller units called tokens. These tokens are often words, but they can also be sub-words or individual characters, depending on the task and desired granularity. For example, the sentence "RNNs process sequences." might be tokenized into ["RNNs", "process", "sequences", "."].
Vocabulary Building: After tokenization, we create a vocabulary, which is a unique set of all tokens present in our training data. Each unique token is assigned a specific integer index. This creates a mapping, like {"RNNs": 1, "process": 2, "sequences": 3, ".": 4, ...}. Special tokens, like <UNK> for unknown words or <PAD> for padding, are often added.
Integer Encoding: Using the vocabulary mapping, each sequence of tokens is converted into a sequence of corresponding integers. Our example sentence becomes [1, 2, 3, 4]. This is the fundamental numerical representation derived from the text.
Handling Variable Lengths (Padding/Truncation): RNNs are often trained in batches for efficiency. However, sequences in a batch typically need to have the same length. Since real text comes in varying lengths, we apply padding (adding special <PAD> tokens, usually represented by 0) or truncation (removing tokens) to standardize the length of all integer sequences within a batch. Masking is a technique used alongside padding to signal to the model which parts of the sequence are actual data and which are just padding, ensuring the padding doesn't affect the model's learning process inappropriately.

The output of these steps is typically a batch of integer sequences, ready for the next stage. While these integer sequences can sometimes be fed directly into an RNN, it's far more common, especially in Natural Language Processing (NLP), to first pass them through an Embedding Layer. This layer, usually trained as part of the main model, transforms each integer index into a dense, lower-dimensional vector (an embedding). These vectors capture semantic similarities between tokens (e.g., 'king' and 'queen' might have similar vectors) and provide a richer, more efficient representation than raw integers or sparse one-hot encodings. We will look into embedding layers in more detail later in this chapter.

The following diagram illustrates this general text preprocessing workflow:

A typical pipeline transforming raw text into numerical tensors suitable for RNN input. The embedding layer step, shown dashed, is often integrated into the model itself rather than being a separate preprocessing stage.

The subsequent sections in this chapter will elaborate on each of these essential steps, providing practical guidance and code examples for implementing them using common libraries. Mastering these techniques is fundamental for applying RNNs effectively to any text-based task.