Contextualized embeddings are representations of words or phrases in a given context that capture their meaning based on the surrounding words and the overall context of the sentence or document. Unlike traditional word embeddings, such as word2vec or GloVe, which represent words as fixed vectors regardless of the context, contextualized embeddings take into account the varying meanings of words based on the context in which they appear.

  Contextualized embeddings are typically generated using deep learning models, such as Transformer-based architectures like BERT (Bidirectional Encoder Representations from Transformers) or GPT (Generative Pre-trained Transformer). These models are trained on large amounts of text data to learn the relationships between words and their contexts.

  The process of generating contextualized embeddings involves several steps. First, the text is tokenized into individual words or subwords. Then, each token is mapped to its corresponding word-vector representation (i.e., word embedding) based on a pretrained model like Word2Vec or GloVe. Next, these word embeddings are fed into the contextualized model, which takes into account the position of each word in the sentence and the surrounding words to generate a unique representation for each token.

  The contextualized model uses attention mechanisms to assign different weights to each word in the context, giving more importance to the words that are semantically closer to the target word. This allows the model to capture the meaning of a word based on how it is used in a particular sentence or document.

  For example, consider the word "bank," which can have different meanings depending on the context. In the sentence "I deposited money in the bank," the context suggests that "bank" refers to a financial institution. However, in the sentence "I sat by the river bank," the context indicates that "bank" refers to the side of the river. Contextualized embeddings capture this distinction by assigning different representations to the word "bank" in each context.

  By considering the context in which words appear, contextualized embeddings enhance the ability to capture the nuanced meaning of words, including polysemy (multiple meanings) and homonymy (same spelling, different meanings). They also enable more accurate downstream tasks like sentiment analysis, natural language understanding, and machine translation, where understanding the meaning of words in context is crucial.