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Related Questions
- How do linguistic features such as part-of-speech, syntax, and morphology affect the performance of text classification models?
- Can you explain the role of semantic information, including word embeddings and semantic roles, in improving the accuracy of text classification models?
- How do pragmatic aspects of language, such as implicature, inference, and figurative language, impact the performance of NLP models in text classification tasks?
- How do models trained on specific domains or genres of text, such as news articles or social media posts, perform differently in terms of contextual information?
- Can you discuss the challenges of capturing contextual information in text classification tasks, particularly for out-of-vocabulary words or words with multiple meanings?
- How do different types of contextual information interact with each other, and how do NLP models handle these interactions?
- What are some techniques for incorporating contextual information into text classification models, such as attention mechanisms or contextualized embeddings?
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