Contextualized embeddings address the issue of ambiguity in language by capturing the meaning of words and phrases based on their surrounding context. Unlike traditional word embeddings that assign fixed vectors to words regardless of context, contextualized embeddings take into account the context in which a word appears, resulting in more nuanced representations.

  By using deep learning models like bidirectional transformers, such as BERT (Bidirectional Encoder Representations from Transformers), contextualized embeddings are trained to predict the next word in a sentence based on the preceding and following words. This unsupervised training allows the model to learn the contextual relationships between words and capture the meaning of words based on their surrounding context.

  This approach helps to address ambiguity because the same word can have different meanings depending on its context. For example, the word "river" can refer to a body of flowing water or the verb "to river" in a different context. Contextualized embeddings distinguish between these different meanings by considering the entire sentence or text, enabling the model to capture the intended sense of the word.

  Furthermore, contextualized embeddings can also handle ambiguous pronouns or references. In a sentence like "He went to the store and bought a book," the pronoun "he" could refer to multiple individuals mentioned earlier. Through contextualized embeddings, the model can understand the antecedent of the pronoun by considering the entire sentence, thus resolving the ambiguity.

  Overall, contextualized embeddings address the issue of ambiguity in language by capturing the meaning of words and phrases based on their context. This enables a more accurate understanding and interpretation of language, helping to address the challenges posed by ambiguity.