Yes, contextualized embeddings are designed to capture the semantics of phrases or sentences. Unlike traditional word embeddings like Word2Vec or GloVe, which assign static representations to words, contextualized embeddings take into account the context in which a word appears. This means that the embedding of a word can vary depending on its surrounding words or the overall sentence.

  Contextualized embeddings models, such as BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pretrained Transformer), and ELMo (Embeddings from Language Models), use deep learning techniques to learn contextual representations of text. These models are usually pre-trained on a large corpus of text, which allows them to capture a wide range of linguistic properties and semantic relationships.

  By considering the context, contextualized embeddings can capture the meaning of words that have different senses or can change their meaning depending on the context. For example, the word "bank" can refer to a financial institution or the side of a river, and contextualized embeddings can differentiate these meanings based on how the word is used in a given sentence.

  Additionally, contextualized embeddings can also capture other semantic information, such as negation or sentiment. For instance, the phrase "not good" would have a different contextualized embedding than the phrase "very good," reflecting the opposite sentiment implied by the negation.

  In summary, contextualized embeddings are designed to capture the semantics of phrases or sentences by considering the surrounding context and are capable of representing different meanings and nuances within a given text.