What is the purpose of embeddings in natural language processing (NLP)?

Embeddings in natural language processing (NLP) serve to convert text into numerical vectors that capture semantic meaning, enabling machine learning models to process language data effectively. Unlike raw text, which is unstructured and ambiguous, embeddings represent words, phrases, or sentences as dense vectors in a continuous space. This numerical format allows models to perform mathematical operations on language, such as measuring similarity between words or identifying patterns in text. For example, the word “cat” might be represented as a 300-dimensional vector, while “dog” could be another vector nearby in the same space, reflecting their related meanings.

The primary strength of embeddings lies in their ability to encode semantic relationships. By analyzing large text corpora, embedding models learn to position words with similar contexts or meanings close to one another in the vector space. For instance, in models like Word2Vec, the vector for “king” minus “man” plus “woman” might result in a vector close to “queen,” demonstrating how arithmetic operations can reflect linguistic relationships. This property enables NLP models to generalize better—for example, understanding that “fast” and “quick” are synonyms even if they haven’t seen both words in every possible context during training. Embeddings also handle polysemy (words with multiple meanings) by relying on context. In models like BERT, the embedding for “bank” changes based on whether it appears in “river bank” or “financial bank,” thanks to attention mechanisms that consider surrounding words.

Beyond semantics, embeddings address practical challenges in NLP. Traditional methods like one-hot encoding create sparse, high-dimensional vectors that are inefficient for computation. Embeddings compress this information into lower-dimensional, dense vectors, reducing memory usage and improving model performance. For example, a vocabulary of 10,000 words represented as 300-dimensional embeddings requires far fewer parameters than a 10,000-dimensional one-hot matrix. This efficiency is critical for tasks like text classification, machine translation, or named entity recognition, where models process large datasets. Pre-trained embeddings (e.g., GloVe or FastText) further streamline development by allowing developers to reuse vectors trained on massive corpora, saving time and computational resources. In summary, embeddings bridge the gap between human language and machine learning by providing structured, efficient, and semantically rich representations of text.