How does the concept of contextual word embeddings, as used in models like BERT, enhance the understanding of word meanings compared to traditional word embeddings?
Tuesday, 11 June 2024
by EITCA Academy
The advent of contextual word embeddings represents a significant advancement in the field of Natural Language Processing (NLP). Traditional word embeddings, such as Word2Vec and GloVe, have been foundational in providing numerical representations of words that capture semantic similarities. However, these embeddings are static, meaning that each word has a single representation regardless of its