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Representation Learning for Natural Language Processing

Representation learning techniques in NLP capture meaningful and distributed representations of textual data, known as embeddings. These embeddings enable NLP models to effectively understand semantic and syntactic relationships. Popular methods include word embeddings like Word2Vec and GloVe, as well as contextualized word embeddings like ELMo and BERT, revolutionizing language understanding and generation tasks.

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