"Contextualized Embeddings" have revolutionized text representation, enabling us to capture the rich nuances of language. A key finding in the field of natural language processing (NLP) reveals that contextualized word embeddings can effectively grasp complex semantic relationships between words and their context. This includes figurative language, idiomatic expressions, and even subtle connotations.
For instance, consider the phrase "It's raining cats and dogs." Here, the word "cats" and "dogs" don't literally refer to the animals, but rather convey the intensity of the rain. Traditional word embeddings, like Word2Vec, might struggle to capture this idiomatic expression, as they focus solely on the individual word's meaning. However, contextualized embeddings, such as BERT or RoBERTa, can analyze the surrounding context and accurately represent the figurative language used.
This breakthrough is made possible by the attention mechanism, which allows contextualized embeddings to we...
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