The Beginner’s Guide to Text Embeddings

The Beginner's Guide to Text Embeddings` discusses the concept of text embeddings, which represent human language to computers. The guide introduces sparse and dense vector techniques for embedding words and longer texts in a way that conveys meaning rather than just their lexical form. Sparse vectors are context-free but have limitations such as being unable to represent unknown words or encode semantics, whereas dense vector techniques like BERT produce vectors with a notion of how words are related to each other and can encode semantics. The guide explains how dense vectors can be visualized using dimensionality reduction techniques like t-SNE and plotted on a two-dimensional grid to show similarities between words and texts. It also discusses how text vectors are used as the basis for modern NLP technologies such as semantic search, translation, question answering, and automatic summarization.

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