What are the main challenges in NLP embeddings?

What are the main challenges in NLP embeddings?

Some key challenges include handling polysemy, managing out-of-vocabulary words, and ensuring contextual relevance.

Can you explain polysemy and why it's a challenge?

Polysemy refers to a single word having multiple meanings. It complicates embeddings as the model must discern context to choose the correct meaning.

How do embeddings deal with out-of-vocabulary words?

Out-of-vocabulary words can be handled using techniques like subword tokenization or by using embeddings for similar words.

What methods can improve the contextual relevance of embeddings?

Methods like contextual embeddings (e.g., BERT, ELMo) and attention mechanisms can significantly enhance contextual relevance.