Which technique is often used to translate text into a numerical form in NLP?

The technique commonly used to translate text into a numerical form in natural language processing (NLP) is embedding. This approach involves mapping words or phrases from a vocabulary to vectors of continuous numbers in a high-dimensional space. This transformation is crucial because most machine learning models operate on numeric data rather than text.

Embeddings capture the semantic relationships between words by placing words with similar meanings closer together in the vector space. For example, the words "king" and "queen" will have more similar vector representations compared to "king" and "car" because they relate to similar concepts. This ability to encode contextual similarity is fundamental in many NLP applications, such as sentiment analysis, translation, and information retrieval.

In contrast, clustering groups data into clusters based on similarity, normalization adjusts the range and distribution of data, and sharding is a method of distributing data across different database instances. While these techniques play important roles in data processing and management, they do not serve the specific purpose of translating text into a numeric format like embeddings do.