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.
This book provides a comprehensive overview of the representation learning techniques for natural language processing. It presents a systematic and thorough introduction to the theory, algorithms and applications of representation learning and shares insights into the future research directions for each topic as well as for the overall field of representation learning for natural language processing.