What are word embeddings in NLP?
An example of an AI chatbot conversation about word embeddings in natural language processing.
What are word embeddings in NLP?
Word embeddings are numerical representations of words that capture their meanings and relationships in a continuous vector space.
How do word embeddings improve NLP tasks?
They enhance tasks like sentiment analysis and machine translation by providing context and semantic similarity between words.
Can you give examples of popular word embedding models?
Sure! Popular models include Word2Vec, GloVe, and FastText, each with unique approaches to generating embeddings.
What are the limitations of word embeddings?
Limitations include inability to handle out-of-vocabulary words and lack of context for polysemous words.
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