Contextual Embeddings

Contextual embeddings are vector representations of words or tokens in natural language processing (NLP) that capture meaning based on their surrounding context in a sentence, rather than having a fixed representation. They are generated by deep learning models like BERT, GPT, and ELMo, which analyze entire sequences to produce embeddings that vary depending on usage. This allows the same word to have different embeddings in different contexts, enabling more nuanced understanding of language semantics.

Also known as: Contextualized Embeddings, Dynamic Word Embeddings, Context-Aware Embeddings, BERT Embeddings, Transformer Embeddings

Why learn Contextual Embeddings?

Developers should learn contextual embeddings when working on advanced NLP tasks such as sentiment analysis, machine translation, question answering, or text classification, where understanding word meaning in context is crucial. They are essential for building state-of-the-art language models and applications that require semantic understanding beyond simple word matching, as they improve accuracy by capturing polysemy and syntactic relationships.

Learning Resources

• docs: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• docs: Hugging Face Transformers Documentation
• course: Stanford CS224N: Natural Language Processing with Deep Learning
• docs: Contextual Word Embeddings: A Contextual Introduction
• course: Fast.ai Course: Practical Deep Learning for Coders