Word embeddings handle homonyms by representing them as distinct vectors in the embedding space. Homonyms are words that have the same spelling but different meanings. In word embeddings, each word is represented as a dense vector in a high-dimensional space, where the relative positions of the vectors capture semantic relationships between words.

  When training word embeddings, the model incorporates the contexts in which words appear to learn their embeddings. As a result, the word embeddings can capture the different meanings of homonyms based on the contexts in which they occur.

  For example, consider the word "bank." It can refer to a financial institution or the side of a river. In a word embedding model, the vectors corresponding to "bank" for each meaning will be different, reflecting their distinct meanings. The model learns to differentiate the two meanings based on the words that typically appear in their contexts. So, words like "money" and "loan" are likely to be associated with the financial institution meaning of "bank," while words like "river" and "shore" are likely to be associated with the river meaning.

  When using word embeddings, if we encounter a homonym in a text, we can use the context to disambiguate its meaning. The similarity between the word vector and the vectors of its neighboring words can give us a clue about which sense of the word is intended. By considering the surrounding words and their embeddings, we can infer the correct meaning of the homonym in that specific context.

  Overall, word embeddings handle homonyms by assigning distinct vector representations to different senses of a word based on the contexts in which they appear in the training data. This allows downstream NLP tasks to leverage this information for better understanding of text.