How do word embeddings work?

Can you explain how word embeddings work in natural language processing?

Sure! Word embeddings are a type of word representation that allows words to be represented as vectors in a continuous vector space.

What are some common algorithms used to create word embeddings?

Common algorithms include Word2Vec, GloVe, and FastText, each using different methods to capture word relationships.

How do these embeddings help in understanding language?

They help by capturing semantic meanings and relationships between words, allowing models to understand context better.

Can you give an example of how word embeddings are used in a real application?

Absolutely! They're used in applications like sentiment analysis, where understanding the context of words is crucial for accurate predictions.