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Related Questions
- Can you explain the concept of word embeddings and how they address polysemy in NLP?
- How do word embeddings handle homographs, and what are some common techniques used to disambiguate them?
- What are some popular word embedding techniques, such as Word2Vec, GloVe, and FastText, and how do they differ in resolving polysemy and homograph issues?
- Can you provide examples of how word embeddings can resolve polysemy and homograph issues in a sentence or paragraph-level context?
- How do word embeddings handle out-of-vocabulary (OOV) words, and what strategies can be employed to handle them effectively?
- Can you discuss the relationship between word embeddings and part-of-speech tagging, and how they can be used together to improve NLP models?
- What are some common challenges and limitations of using word embeddings to resolve polysemy and homograph issues, and how can they be addressed?
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