Contextualised Word Representations

In the field of natural language processing, Contextualized Word Representations are an advanced word embedding technique designed to generate dynamic word vector representations by considering the contextual environment. Unlike traditional static word embeddings, this approach can capture subtle differences in word meanings across different contexts, thereby more accurately reflecting the meaning of words. Its core objective is to deepen and broaden the model's understanding of language and enhance semantic expression capabilities. Contextualized Word Representations have shown significant advantages in applications such as machine translation, sentiment analysis, and question-answering systems, effectively improving system performance and robustness.