This explores the challenges and recent advancements in natural language
processing (NLP), a field of artificial intelligence focused on enabling
computers to understand and use human language. The text uses a simple
restaurant story to illustrate the complexities of language
understanding, highlighting the need for sophisticated linguistic skills
and world knowledge in AI systems. It then details the significant
progress made in speech recognition using deep learning, contrasting
this success with ongoing challenges in tasks like sentiment
classification and question answering. The chapter introduces recurrent
neural networks (RNNs) and word embedding techniques like Word2Vec,
explaining how these methods represent words numerically to improve NLP
performance, while acknowledging the presence of societal biases within
these models. Finally, the text discusses the potential for encoding
entire sentences and documents as vectors to further enhance semantic
understanding.
