What are the main challenges in word embeddings?

What are the main challenges in word embeddings?

Some challenges include handling polysemy, capturing context, and dealing with out-of-vocabulary words.

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

Polysemy refers to a word having multiple meanings. It complicates embeddings as a single vector may not capture all meanings.

How do context and word embeddings relate?

Context helps determine the meaning of a word in a specific situation, which is crucial for creating accurate embeddings.

What are some solutions to these challenges?

Solutions include using context-aware models like BERT, which generate embeddings based on surrounding words.