What are the different embedding techniques used in NLP?
An example of an AI chatbot conversation about embedding techniques in NLP.
What are the different embedding techniques used in NLP?
In NLP, common embedding techniques include Word2Vec, GloVe, and FastText. Would you like to know more about any specific one?
Can you explain how Word2Vec works?
Word2Vec uses neural networks to learn word associations from a large corpus of text. It can represent words in a continuous vector space.
What are the advantages of using GloVe over Word2Vec?
GloVe captures global statistical information of the corpus, while Word2Vec focuses on local context. This can lead to better representations in some cases.
How does FastText improve upon Word2Vec?
FastText represents words as bags of character n-grams, allowing it to generate embeddings for out-of-vocabulary words and capture subword information.
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