Data Science with Python Certification Data Science with Python Natural Language Processing 2

Question 1

Which scikit-learn class converts a collection of text documents to a matrix of TF-IDF features?

Accepted Answer

TfidfVectorizer

Answer

CountVectorizer

Answer

HashingVectorizer

Answer

LabelEncoder

Question 2

What is a bigram in NLP?

Accepted Answer

A sequence of two consecutive words used as a single feature

Answer

A pair of characters used in character-level modeling

Answer

A document encoded with two separate embedding methods

Answer

A two-layer neural network for text classification

Question 3

In spaCy, what attribute of a `Token` object returns its part-of-speech tag?

Accepted Answer

token.pos_

Answer

token.lemma_

Answer

token.dep_

Answer

token.ent_type_

Question 4

What is the purpose of padding sequences when preparing text data for deep learning models?

Accepted Answer

To make all input sequences the same length for batch processing

Answer

To remove rare words below a frequency threshold

Answer

To encode words as one-hot vectors

Answer

To apply dropout regularization to embeddings

Question 5

Which method on a scikit-learn vectorizer both learns the vocabulary and transforms the training data in one step?

Accepted Answer

fit_transform()

Answer

transform()

Answer

fit()

Answer

partial_fit()

Question 6

What does Named Entity Recognition (NER) identify in text?

Accepted Answer

Real-world entities such as persons, organizations, and locations

Answer

Grammatical dependencies between words in a sentence

Answer

The sentiment polarity of each sentence

Answer

Duplicate or near-duplicate sentences in a corpus

Question 7

When using `CountVectorizer` in scikit-learn, what does setting `ngram_range=(1, 2)` do?

Accepted Answer

Includes both unigrams and bigrams as features in the document-term matrix

Answer

Limits the vocabulary to words appearing between 1 and 2 times

Answer

Restricts documents to a length of 1 to 2 sentences

Answer

Applies L1 and L2 normalization to the feature vectors