Natural Language Processing: Semantic Representation and Embeddings

What this episode covers

This episode explore the landscape of natural language processing (NLP), beginning with fundamental concepts and challenges like ambiguity and figurative language. The discussion progresses to vector space models (VSMs) for representing word meaning, contrasting early count-based techniques with more recent predictive models like Word2vec and GloVe, including advancements in handling unseen words and incorporating external knowledge. The text then examines contextualized embeddings, a significant evolution that allows word representations to adapt to their surrounding text, focusing on Transformer-based models like BERT and its extensions, alongside analysis of their linguistic understanding and evaluation methods. Finally, the sources touch upon representing larger text units like sentences and documents, covering both unsupervised and supervised techniques and their applications

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