Contextualized embeddings differ from traditional word embeddings in the way they capture the meaning of a word within its context. Traditional word embeddings, such as word2vec and GloVe, assign a fixed vector representation to each word in the vocabulary regardless of the surrounding words or the specific context in which the word appears. These embeddings represent the words as discrete units and do not account for variations in meaning based on the context.

  On the other hand, contextualized embeddings, such as ELMo, GPT, and BERT, provide word representations that are sensitive to the context in which the words occur. These models use deep learning architectures, such as recurrent neural networks (RNNs) or transformers, to generate contextualized vectors for each word based on the surrounding words. This means that the same word can have different embeddings depending on the context in which it appears.

  Contextualized embeddings capture complex semantic relationships and can better represent the meaning of words in different contexts. They are trained on large-scale language modeling tasks, which expose the models to a wide range of sentences and contexts, enabling them to learn the variations in word meaning and capture syntactic and semantic patterns.

  In summary, while traditional word embeddings provide fixed vector representations for words independent of context, contextualized embeddings adapt their representations based on the surrounding words and context, thereby capturing more nuanced meaning and improving performance in various natural language processing tasks.