How to generate embeddings

In the field of natural language processing (NLP), embeddings have become a widely adopted technique for representing text data in a format that can be effectively processed by machine learning models. These vector representations aim to capture the semantic and syntactic relationships between words, enabling models to reason about textual data in meaningful ways. While embeddings have significantly advanced the capabilities of NLP systems, it is important to understand the technical process behind generating these representations. In this blog post, we will provide a detailed explanation of the step-by-step procedure for transforming raw text into dense vector embeddings.

Step 1: Tokenization – Breaking Down the Text

Before we can generate embeddings, we must first break down the raw text into smaller units called tokens. This process, known as tokenization, is the foundation upon which the entire embedding generation process is built.

Tokenization can be performed at different levels, depending on the specific requirements of the task and the embedding model being used. The most common approach is word-level tokenization, where the text is split into individual words. However, in some cases, character-level or subword-level tokenization may be more appropriate, especially when dealing with out-of-vocabulary words or morphologically rich languages.

Once the tokenization process is complete, we are left with a sequence of tokens that can be processed by the embedding model.

Step 2: Vocabulary Creation – Indexing the Lexicon

With the tokens in hand, the next step is to create a vocabulary – a comprehensive list of all unique tokens present in the text data. Each token is assigned a unique index within this vocabulary, serving as its numerical identifier.

The size of the vocabulary can vary greatly depending on the domain and the complexity of the text data. Some models may have vocabularies with tens or hundreds of thousands of tokens, while others may opt for smaller, more compact vocabularies to reduce computational complexity.

It's important to note that the vocabulary creation process is not a one-time affair. As new text data is encountered, the vocabulary may need to be updated to include previously unseen tokens. Strategies like unknown token handling and character-level embeddings can help mitigate the impact of out-of-vocabulary words.

Step 3: One-Hot Encoding – The Initial Sparse Representation

With the vocabulary in place, the next step is to represent each token numerically. The most straightforward approach is to use a one-hot encoding, where each token is represented as a sparse vector with the same length as the vocabulary size. In this vector, all values are set to zero, except for the index corresponding to the token, which is set to one.

For example, if the vocabulary size is 10,000, and the token "cat" is assigned the index 537, its one-hot encoding would be a vector of 10,000 dimensions, with all values set to zero except for the 537th dimension, which would be set to one.

While one-hot encodings are simple and easy to understand, they suffer from a major drawback: they fail to capture any semantic or syntactic relationships between words. In this sparse representation, each word is treated as a completely independent entity, with no connections to other words in the vocabulary.

Step 4: The Embedding Layer – Transforming Sparsity into Dense Representations

Here is where the magic truly begins. To overcome the limitations of one-hot encodings and capture the rich relationships between words, we introduce the embedding layer – a crucial component of most modern NLP models.

The embedding layer is a matrix of weights, where each row corresponds to the embedding vector for a particular token in the vocabulary. The size of this matrix is (vocabulary_size, embedding_dimension), where the embedding_dimension is a hyperparameter that determines the dimensionality of the embeddings.

During the training process, the weights of this embedding matrix are learned and adjusted to capture the semantic and syntactic relationships between words based on their contexts in the training data. This learning process is facilitated by techniques like backpropagation and gradient descent, which iteratively update the embedding vectors to minimize the overall loss or error of the model.

The key aspect of the embedding layer is that it transforms the sparse one-hot encodings into dense vector representations, where each dimension captures some aspect of the word's meaning and usage. Words with similar meanings or contexts tend to have similar embedding vectors, while words with different meanings have dissimilar vectors.

Step 5: Embedding Lookup – Retrieving the Rich Representations

With the embedding layer in place, generating embeddings for individual tokens or sequences of tokens becomes a relatively straightforward process. It involves looking up the corresponding embedding vectors in the embedding matrix and retrieving their dense vector representations.

For individual tokens, the process is as simple as using the token's index in the vocabulary to retrieve the corresponding row from the embedding matrix. This row represents the embedding vector for that token, encapsulating its semantic and syntactic information.

For sequences of tokens, such as sentences or documents, the individual token embeddings are typically combined using various techniques, such as summing, averaging, or more advanced sequence models like recurrent neural networks (RNNs) or transformers. These combined embeddings aim to capture the overall meaning and context of the entire sequence.

Step 6: Normalization (Optional) – Ensuring Consistent Scale

Depending on the specific application and the embedding model being used, the generated embeddings may undergo an optional normalization step. This step ensures that the embedding vectors have a consistent scale or length, which can be beneficial for certain downstream tasks or algorithms.

One common normalization technique is L2 normalization, where each embedding vector is divided by its L2 norm (the square root of the sum of squared values), ensuring that all vectors have a unit length. This normalization can help mitigate the impact of outliers or numerical instabilities during training and inference.

Step 7: Downstream Task Integration – Leveraging the Rich Representations

While the process of generating embeddings is fascinating in itself, the true power of embeddings lies in their ability to enhance the performance of various downstream NLP tasks. These tasks can range from text classification and sentiment analysis to machine translation, question answering, and beyond.

In most modern NLP architectures, the generated embeddings serve as input representations to larger neural network models, which are trained to perform the specific task at hand. By leveraging the rich semantic and syntactic information captured by the embeddings, these models can reason about the text data more effectively, leading to improved accuracy and performance.

Conclusion

The process of generating embeddings is an evolving journey that begins with raw text and culminates in rich, dense vector representations that capture the intricate relationships and nuances of human language. From tokenization and vocabulary creation to one-hot encoding, the embedding layer, and embedding lookup, each step plays a crucial role in transforming textual data into a format that can be effectively processed by machine learning models.

While the underlying mathematics and algorithms may seem complex, the core idea behind embeddings is remarkably simple: to represent words in a way that reflects their semantic and syntactic similarities, enabling machines to understand and reason about language in a more human-like manner.