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Related Questions
- Can machine learning models be trained to recognize and interpret homographs and homophones that convey irony?
- How do machine learning models currently handle homographs and homophones that have multiple meanings, and what are the limitations of their approaches?
- What techniques can be used to improve the ability of machine learning models to detect and understand irony in language, particularly in cases where homographs and homophones are involved?
- Can machine learning models be fine-tuned to recognize the context in which homographs and homophones are used to convey irony, and if so, how?
- What role does semantic analysis play in helping machine learning models to understand homographs and homophones that convey irony?
- Can machine learning models be designed to learn from labeled data that includes examples of homographs and homophones used to convey irony, and if so, how?
- How do machine learning models handle the nuances of human language, such as sarcasm and understatement, which often rely on homographs and homophones to convey meaning?
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