Embedding vectors represent each word or token as a dense, multi-dimensional numerical vector. The size of these vectors is chosen before training begins, and their values are learned automatically by the model during training. Instead of explicitly encoding meaning, each dimension captures abstract features that are useful for the model’s task. Although the individual values in an embedding vector are not directly interpretable by humans, together they encode rich semantic information. Because these vectors often contain hundreds or even thousands of dimensions, they are difficult to visualize directly.
One of the most important properties of embedding vectors is that they capture semantic similarity. Words with similar meanings tend to have vectors that are close to each other in the embedding space. For example, words like “laptop” and “computer” will have very similar embeddings, while words such as “orange” and “banana” will be close to each other because they belong to the same semantic category. This structure allows models to generalize better and understand relationships between words.
To measure similarity between embedding vectors, models commonly use the dot product. This operation multiplies corresponding values in two vectors and sums the results. A large positive dot product indicates that two words are semantically similar, a value close to zero suggests little or no relationship, and a negative value can indicate opposing or contrasting meanings.
Embedding vectors can also encode more complex relationships and analogies. A classic example involves gender relationships: the vector difference between “woman” and “man,” when added to the vector for “king,” results in a vector close to that of “queen.” Similarly, geographical relationships can emerge, such as the relationship between “Rome” and “Paris” being comparable to that between “Italy” and “France.” These patterns emerge naturally from the way embeddings are learned from large amounts of text data.
There are two main ways to obtain word embeddings. One option is to use pre-trained embedding models, which are trained independently on massive text corpora and can then be reused across different applications. Popular examples include models like GloVe. Another option is to learn embeddings directly as part of a larger model. In Transformer-based architectures, for instance, embeddings are learned during training along with the rest of the model, making separate embedding libraries unnecessary.
In practice, using embedding vectors involves tokenizing an input sentence and converting each token into its corresponding embedding vector. These vectors can then be processed by a neural network. While it is possible to flatten these vectors and use them in a standard feedforward network, text data is sequential by nature. For this reason, specialized architectures designed for sequential data, such as recurrent networks and Transformers, are usually more effective.
In summary, embedding vectors provide a compact and meaningful way to represent words numerically, enabling models to capture semantic similarity and complex relationships in language. They are a fundamental component of modern natural language processing systems and form the basis for more advanced architectures that handle text effectively.