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Related Questions
- Can machine learning models handle nuances in word meanings, such as polysemy, and if so, how?
- How do models distinguish between homographs, which have the same spelling but different meanings, during language processing?
- What techniques do machine learning models use to resolve ambiguities in word meanings, such as when a word has multiple related but distinct senses?
- In what ways do models address the challenge of polysemy in natural language processing, and what are some common strategies used to tackle this issue?
- Can you explain how models handle homographs with multiple related senses, such as 'bank' referring to both a financial institution and the side of a river?
- What is the impact of polysemy and homographs on the performance of machine learning models in language tasks, such as sentiment analysis or text classification?
- How do models adapt to learn the nuances of language, including polysemy and homographs, when trained on large datasets and fine-tuned on specific tasks?
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