What is the technique of condensing a language vocabulary into smaller dimensional vectors called?

The technique referred to in the question, which involves condensing a language vocabulary into smaller dimensional vectors, is known as embedding. In natural language processing and machine learning, embedding transforms large, sparse vector representations of words into dense vectors. These dense vectors capture semantic relationships between words, allowing them to be represented in a way that models their meanings in a lower-dimensional space.

For instance, word embeddings such as Word2Vec or GloVe assign similar vector representations to words that share similar contexts in large text corpora, making it easier for models to process and analyze text data. This process enhances the efficiency of machine learning algorithms since operating in lower-dimensional spaces typically leads to more manageable computations and potentially improved model performance.

The other options represent different concepts. Vectorization usually refers to the process of converting text into numerical form, while dimensionality reduction focuses on reducing the number of features in a dataset, which may not necessarily be specifically aimed at language vocabulary. Word representation is a broader term that could encompass embeddings, but it does not specifically denote the technique of condensing vocabulary into smaller vectors. Therefore, embedding is the most accurate term for this technique.