In general, word embedding is a technique used in natural language processing (NLP) to map words or phrases from a vocabulary to a continuous vector space. The goal of word embedding is to capture the meaning of a word in a vector representation, such that similar words have similar representations and dissimilar words have dissimilar representations.
- For example, the word “night” might be represented as
(-0.076, 0.031, -0.024, 0.022, 0.035).
- It means that if there are two words used similarly in the text, they will have the same vector representations.
- After mapping words into the vector space, we can use vector math to find words with similar semantics.
OpenAI’s embedding implementation helps the ChatGPT model to interpret words based on categories and their numerical relation to those categories. In other words, OpenAI’s text embeddings measure the relatedness of text strings.
Embeddings are commonly used for:
- Search (where results are ranked by relevance to a query string).
- Clustering (where text strings are grouped by similarity).
- Recommendations (where items with related text strings are recommended).
- Anomaly detection (where outliers with little relatedness are identified).
- Diversity measurement (where similarity distributions are analyzed).
- Classification (where text strings are classified by their most similar label)
- Translation (where the embeddings of the words in the source language can be used to initialize the embeddings of the words in the target language)