Contextualized embeddings handle negation in language by capturing the context in which negation occurs. These embeddings consider the surrounding words and their order to determine the meaning of the negation. Let me explain in more detail.

  Contextualized embeddings, such as BERT (Bidirectional Encoder Representations from Transformers), capture the meaning of words based on their context. They use deep neural networks to generate word representations that take into account the surrounding words in a sentence.

  When it comes to negation in language, contextualized embeddings are able to capture the effect of negation by incorporating the context of the negated word. For example, in the sentence "I'm not happy," the word "not" is a negation term that changes the meaning of "happy." Contextualized embeddings can understand this relationship by considering the entire sentence and the relationship between "not" and "happy."

  Through the training process, contextualized embeddings learn to differentiate between negated and non-negated words based on the surrounding context. They are able to encode information that reflects the presence of negation and its impact on the meaning of words. This allows them to generate embeddings that capture the nuanced meaning of negation in language.

  It is important to note that contextualized embeddings are typically pre-trained on large corpora of text using unsupervised learning. This means they learn from a wide range of texts and can capture various language phenomena, including negation. However, the extent to which they handle negation effectively may vary depending on the specific pre-training and fine-tuning process.

  In summary, contextualized embeddings handle negation in language by considering the surrounding context and capturing the relationship between negation terms and the words they modify. They are able to generate embeddings that reflect the impact of negation on the meaning of words, allowing for more accurate interpretation and understanding of negated statements.