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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?
To use NLTK's machine learning algorithms
To combine NLTK's text preprocessing with Scikit-Learn's ML models
To replace Scikit-Learn's vectorizers
To speed up NLTK corpus downloads
2. Which NLTK functionalities are commonly integrated into Scikit-Learn preprocessing pipelines?
word_tokenize (tokenization)
PorterStemmer (stemming)
TfidfVectorizer (vectorization)
WordNetLemmatizer (lemmatization)
3. Scikit-Learn's CountVectorizer can accept a custom tokenizer function from NLTK (e.g., word_tokenize).
True
False
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?
GridSearchCV
Pipeline
StandardScaler
ConfusionMatrixDisplay
6. Select all steps that might be part of a NLTK-Scikit-Learn pipeline for text classification.
Removing stopwords using NLTK's stopwords corpus
Lemmatizing tokens with NLTK's WordNetLemmatizer
Converting text to features with Scikit-Learn's CountVectorizer
Training a Naive Bayes classifier with NLTK's classify module
7. To integrate NLTK's stemming into Scikit-Learn, you must always modify the original text data outside of a Pipeline.
True
False
8. What parameter of Scikit-Learn's TfidfVectorizer would you use to incorporate NLTK's tokenizer?
preprocessor
tokenizer
stop_words
ngram_range
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?
Inherit from BaseEstimator
Implement a fit method
Use NLTK's download function
Implement a transform method
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