Natural Language Processing

The video explores the significance of language and its components, introducing natural language processing (NLP) and its two main areas: understanding and generation. It discusses challenges like ambiguity and context, and explains concepts like morphology and distributional semantics. The video introduces encoder-decoder models and the use of recurrent neural networks (RNNs) in NLP, highlighting the process of training models to predict words and understand language.

What are the two main ideas explored in natural language processing?

Explain the difference between natural language understanding and natural language generation.

How do we assign meaning to words in language?

What is the significance of context in understanding the meaning of a word?

Describe how distributional semantics helps in understanding word meanings.

What is a count vector and how is it used in natural language processing?

How does an encoder-decoder model function in the context of language modeling?

Discuss the challenges of using words in models for natural language processing.

What role does a recurrent neural network (RNN) play in processing language?

What are some potential applications of natural language processing mentioned in the text?