Contextualized embeddings, such as BERT (Bidirectional Encoder Representations from Transformers), are designed to take into account the surrounding context of each word in a sentence. This means that words with different connotations can be represented differently based on their context.

  In BERT, each word is represented by a contextualized embedding that captures both its meaning and its relationship to the surrounding words. This is achieved through a process called "masked language modeling", where some words in the sentence are randomly masked and the model is trained to predict the original words based on the context. By forcing the model to understand the context, it can learn to differentiate between words with different connotations.

  For example, consider the word "cool", which can have different connotations depending on the context. In the sentence "The weather is cool today", the word "cool" might have a positive connotation, indicating a pleasant temperature. On the other hand, in the sentence "She gave him a cool response", the word "cool" might have a negative connotation, indicating a lack of warmth or enthusiasm.

  In BERT, the word "cool" will have different contextualized embeddings in these two sentences, reflecting the different connotations. The model will learn to capture the sentiment or meaning associated with the word based on the context in which it appears. This allows the model to better understand the intended meaning of words with different connotations and generate more accurate representations.

  It is important to note that while contextualized embeddings can help capture different connotations, the model's understanding is still based on the training data it was exposed to. If the training data is biased or lacks certain connotations, the model may not accurately capture all the nuances. Therefore, it is crucial to ensure diverse and representative training data to improve the model's ability to handle words with different connotations.