Contextualized embeddings handle words with similar meanings by capturing their contextual nuances. Traditional word embeddings like Word2Vec or GloVe assign a fixed vector representation to each word based on the word's co-occurrence statistics in a large corpus. However, these embeddings treat each occurrence of a word exactly the same, regardless of its context or meaning in a specific sentence.

  In contrast, contextualized embeddings, such as ELMo (Embeddings from Language Models) or BERT (Bidirectional Encoder Representations from Transformers), generate word representations that are sensitive to their surrounding context. These models are pre-trained on a large amount of text and learn to predict the next word in a sentence or to fill in a masked word based on the surrounding context.

  When handling words with similar meanings, contextualized embeddings can differentiate between them based on their specific usage in a sentence. For example, consider the words "cool" and "chilly." These words may have similar vector representations in traditional word embeddings, but in a sentence like "The weather is cool today," the word "cool" would have a different contextualized embedding than in the sentence "The chili is chilly." The embeddings for "cool" in these two sentences would capture the different meanings and nuances of the word based on their specific contexts.

  By considering the context in which a word appears, contextualized embeddings can effectively disambiguate words with multiple meanings or words that have similar semantic representations. This ability allows downstream natural language processing tasks, such as sentiment analysis or machine translation, to benefit from the contextual information embedded within the word representations.