What are the differences between various embedding techniques?

What are the differences between various embedding techniques?

There are several embedding techniques like Word2Vec, GloVe, and BERT. Word2Vec uses a shallow neural network to create word embeddings based on context, while GloVe focuses on global word co-occurrence statistics. BERT, on the other hand, is a transformer-based model that captures context in a more nuanced way.

Can you explain how Word2Vec works?

Sure! Word2Vec uses two main architectures: Continuous Bag of Words (CBOW) and Skip-Gram. CBOW predicts a target word based on its context, while Skip-Gram does the opposite by predicting context words from a target word.

What are the advantages of using BERT over Word2Vec?

BERT captures context more effectively because it considers the entire sentence rather than just local context. It also handles polysemy better, meaning it can differentiate between words with multiple meanings based on their usage.

Are there any downsides to using BERT?

Yes, BERT is computationally intensive and requires more resources for training and inference compared to simpler models like Word2Vec.