What are the challenges of synonym replacement in NLP?

What are the main challenges of synonym replacement in NLP?

Some challenges include context sensitivity, polysemy, and maintaining semantic meaning.

Can you explain how context sensitivity affects synonym replacement?

Context sensitivity means that the meaning of a word can change based on surrounding words, making it hard to choose the right synonym.

What is polysemy and how does it complicate synonym replacement?

Polysemy refers to a word having multiple meanings. This complicates synonym replacement because the wrong synonym can alter the intended meaning.

Are there any techniques to overcome these challenges?

Yes, techniques like word embeddings, context-aware models, and semantic analysis can help address these challenges.

How effective are these techniques in real-world applications?

They can be quite effective, but their success often depends on the specific context and the quality of the training data.