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Related Questions
- How do embedding techniques, such as word2vec and GloVe, help reduce the effects of polysemy in NLP models?
- What are some common methods used to disambiguate word senses in polysemous words, and how do they contribute to improved model performance?
- Can you explain the concept of semantic role labeling and how it helps mitigate polysemy in NLP tasks?
- What is the role of context in disambiguating polysemous words, and how do models incorporate contextual information to address this challenge?
- How do models handle homograph polysemy, where a single word has multiple senses with different meanings?
- What is the relationship between polysemy and the concept of polysemous words, and how do models account for this in their architectures?
- Can you discuss the use of explicit sense induction methods, such as supervised and unsupervised learning approaches, to address polysemy in NLP models?
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