Embedding models capture semantic meaning by converting words, phrases, or documents into numerical vectors that reflect their contextual relationships. These models are trained on large text datasets to identify patterns in how language is used. During training, words that appear in similar contexts—like "cat" and "kitten" in sentences about pets—are mapped to vectors that are closer together in a high-dimensional space. This proximity reflects their semantic similarity. For example, Word2Vec, a classic embedding technique, uses a neural network to predict surrounding words in a sentence, forcing the model to learn vector representations that encode contextual meaning. The result is that words with related meanings or functions end up clustered in the vector space.
The structure of the model and its training objective play key roles in capturing semantics. Transformer-based models like BERT use attention mechanisms to weigh the importance of different words in a sentence, enabling them to capture nuanced relationships. For instance, BERT processes bidirectional context, meaning it considers words before and after a target word simultaneously. This allows it to distinguish between homonyms like "bank" (financial institution vs. river edge) based on surrounding terms. Similarly, sentence embeddings from models like Sentence-BERT are fine-tuned to ensure that entire phrases with similar meanings—such as "How old are you?" and "What is your age?"—have closely aligned vectors. The training process optimizes these vectors to reflect semantic equivalence, even when surface-level wording differs.
The practical value of embeddings lies in their ability to represent semantic relationships mathematically. Developers can use cosine similarity to measure how closely related two words or sentences are. For example, in a search application, querying "automobile" might retrieve documents containing "car" because their vectors are nearby. Embeddings also enable arithmetic operations on concepts: the vector for "king" minus "man" plus "woman" yields a result close to "queen," demonstrating how relationships like gender roles are encoded. These properties make embeddings useful for tasks like recommendation systems (matching user preferences to products) or chatbots (understanding user intent). By translating language into a geometric space, embedding models turn abstract semantic relationships into concrete, computable patterns.