Contextualized embeddings are designed to handle polysemous words, which are words that have multiple meanings. These embeddings take into account the context in which a word appears, allowing for different representations of the word based on its surrounding words and sentence structure.

  Traditional word embeddings, such as word2vec or GloVe, treat each word as a single vector representation regardless of its context. This can lead to ambiguous representations for polysemous words, as all of their meanings are collapsed into a single vector.

  In contrast, contextualized embeddings, such as BERT or GPT, generate word representations that are dependent on the surrounding words in a given sentence or phrase. These models use a transformer architecture or similar methods to encode sentence context information.

  When encountering a polysemous word, contextualized embeddings generate different representations for each meaning based on the surrounding context. For example, consider the word "bank." In the sentence "I went to the bank to deposit some money," the word "bank" would be represented differently from its representation in the sentence "I sat by the river bank and enjoyed the view."

  By capturing the contextual information, contextualized embeddings enable downstream natural language processing tasks to better understand the appropriate meaning of a polysemous word in a given context. This can improve performance in tasks such as sentiment analysis, named entity recognition, and machine translation, where word sense disambiguation is crucial.

  It is important to note that the effectiveness of contextualized embeddings in handling polysemous words depends on the size and diversity of the training data used. Large-scale pretraining on a wide variety of texts can help the models learn to disambiguate different word senses based on context. However, there may still be cases where the specific context does not provide enough information for accurate disambiguation, leading to some ambiguity.