Integrating NLTK and Scikit-Learn

Assess your knowledge of building NLP models using text features and ML pipelines.

1. What is a primary reason for integrating NLTK with Scikit-Learn?
2. Which NLTK functionalities are commonly integrated into Scikit-Learn preprocessing pipelines?
3. Scikit-Learn's CountVectorizer can accept a custom tokenizer function from NLTK (e.g., word_tokenize).
4. Name the Scikit-Learn base class that custom transformers (used to integrate NLTK steps) typically inherit from, abbreviated as BE.
5. Which Scikit-Learn component is essential for chaining NLTK preprocessing steps and a machine learning model?
6. Select all steps that might be part of a NLTK-Scikit-Learn pipeline for text classification.
7. To integrate NLTK's stemming into Scikit-Learn, you must always modify the original text data outside of a Pipeline.
8. What parameter of Scikit-Learn's TfidfVectorizer would you use to incorporate NLTK's tokenizer?
9. What NLTK corpus is commonly used to access stopwords for text preprocessing in a Scikit-Learn pipeline?
10. Which of the following are required to create a custom NLTK-based text transformer for Scikit-Learn?
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