What are the limitations of word embeddings?

What are the limitations of word embeddings?

Word embeddings, while powerful, have several limitations. They often fail to capture context, can reinforce biases, and may not represent rare words well.

Can you explain how they reinforce biases?

Certainly! Word embeddings learn from large datasets, which may contain societal biases. For example, if a dataset associates certain professions with specific genders, the embeddings can reflect and perpetuate those biases.

Are there alternatives to word embeddings?

Yes, alternatives include contextual embeddings like BERT and ELMo, which consider the context of words in sentences, providing more nuanced representations.

How do contextual embeddings improve upon traditional word embeddings?

Contextual embeddings generate different representations for the same word based on its context, allowing for a better understanding of meaning in various situations.

What are some practical applications of these embeddings?

They are used in various applications like sentiment analysis, machine translation, and information retrieval, enhancing the performance of NLP tasks.