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@Vikram Basu
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What is the importance of data exploration and visualization in text mining?
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What are the key components of model building in NLP?
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What types of machine learning classifiers are commonly used in text mining?
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How do sequence models differ from traditional classifiers in NLP?
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What metrics are used to evaluate the performance of NLP models?
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Why is preprocessing crucial in text mining?
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What are some common steps involved in text preprocessing?
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What is the role of stopwords in text processing?
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How do word embeddings represent meaning in text?
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What is the goal of using dense embedding vectors in NLP?
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What are the differences between word embeddings and sentence embeddings?
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What is the significance of normalization in text preprocessing?
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How does tokenization facilitate text analysis?
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What challenges might arise during the preprocessing of textual data?
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Why is feature selection important in model building?
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What is the purpose of generative models in NLP?
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How do evaluation methods differ for generative models compared to traditional classifiers?
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What is the relationship between text mining and natural language processing?
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What are the implications of using advanced models like LSTMs in NLP?
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How can data visualization enhance the understanding of text data?
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What are the ethical considerations in text mining and NLP?
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What is the significance of using a diverse set of models in NLP tasks?
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