Contextual embeddings, such as those generated by BERT (Bidirectional Encoder Representations from Transformers), differ from traditional embeddings like Word2Vec in that they capture the meaning of words in their specific context. This means that the embedding for a word changes based on the surrounding words in the sentence.
For example, the word "bank" would have different embeddings in "river bank" and "financial bank" because BERT considers the entire sentence when generating embeddings. This is achieved through a transformer-based architecture that processes text bidirectionally, enabling the model to capture nuanced relationships.
In contrast, traditional embeddings like Word2Vec and GloVe assign a single, static vector to each word, regardless of its usage or context. Contextual embeddings are more powerful for tasks like question answering, named entity recognition, and sentiment analysis, where the meaning of words depends on their context.