Embeddings support text similarity tasks by converting text into numerical vectors that capture semantic meaning. These vectors represent words, phrases, or documents in a high-dimensional space where similar texts are positioned closer together. Unlike traditional methods like keyword matching, embeddings account for context and meaning. For example, the words “car” and “vehicle” might have similar vectors, even if they don’t share letters. This allows systems to recognize relationships between terms that aren’t obvious from surface-level features. By translating text into this structured numerical form, embeddings enable mathematical comparisons (like cosine similarity) to quantify how alike two pieces of text are semantically.
The process starts by using pre-trained models (e.g., Word2Vec, BERT, or FastText) to generate embeddings. These models are trained on large text corpora to learn associations between words based on their co-occurrence or contextual patterns. For instance, BERT creates contextual embeddings where the same word (like “bank”) has different vectors depending on whether it appears in “river bank” or “bank account.” Once text is converted into vectors, similarity is measured using distance metrics. Cosine similarity, for example, calculates the angle between two vectors: a smaller angle (closer to 1) indicates higher similarity. Developers can implement these metrics in code libraries like NumPy or scikit-learn to compare embeddings efficiently, even across large datasets.
Practical applications include search engines, recommendation systems, and chatbots. In a search engine, embeddings allow queries like “affordable electric cars” to match documents mentioning “cheap EVs” without relying on exact keyword overlap. Recommendation systems might use embeddings to group similar product descriptions or user reviews. For example, a user reading about “machine learning tutorials” could receive recommendations for articles tagged “AI education” if their embeddings are close. Embeddings also help detect paraphrases or duplicate content by identifying texts with nearly identical vectors. By leveraging these techniques, developers can build systems that understand semantic relationships, improving accuracy over rule-based or keyword-driven approaches.