The evolution from static word embeddings (like Word2Vec) to contextual embeddings (like BERT) marks a major paradigm shift in Computational Linguistics. Static vectors ...
The evolution from static word embeddings (like Word2Vec) to contextual embeddings (like BERT) marks a major paradigm shift in Computational Linguistics. Static vectors assign a single, fixed meaning representation to a word regardless of its usage.
In contrast, contextual models leverage the Transformer architecture to dynamically generate an embedding based on the surrounding text. This allows the model to differentiate meanings in polysemous words, such as "bank" (river bank vs. financial bank). This advancement is crucial for downstream NLP tasks, including classification and Question Answering, as it captures the nuance and semantic richness of human language with unprecedented accuracy.