What is word embedding?
An example of an AI chatbot conversation about word embedding in natural language processing.
What is word embedding?
Word embedding is a technique in natural language processing that represents words as vectors in a continuous vector space.
How does word embedding work?
It works by mapping words to vectors based on their meanings and contexts, allowing similar words to have similar representations.
What are some popular word embedding models?
Some popular models include Word2Vec, GloVe, and FastText, each using different methods to create word vectors.
Why is word embedding important?
It's important because it captures semantic relationships between words, improving the performance of various NLP tasks.
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