DSE - Data Science Natural Language Processing Fundamentals Questions and Answers

Question 1

A data scientist is preprocessing a large corpus of text for a machine learning model.
They need to reduce words like 'running', 'ran', and 'runs' to a common base form.
Which technique uses a dictionary and morphological analysis to convert a word to its true base form, ensuring the result is a valid word?

Accepted Answer

Lemmatization

Answer

Tokenization

Answer

Stop Word Removal

Answer

Stemming

Question 2

In text analysis, which method assigns a higher weight to words that are frequent in a specific document but are rare across the entire collection of documents, effectively filtering out common words?

Accepted Answer

Term Frequency-Inverse Document Frequency (TF-IDF)

Answer

Bag-of-Words (BoW)

Answer

Word2Vec

Answer

One-Hot Encoding

Question 3

A data scientist is building a system to extract key information from news articles. The system needs to identify and label entities such as 'Google' as an ORGANIZATION, 'London' as a LOCATION, and '2024' as a DATE. Which NLP task is specifically designed for this purpose?

Accepted Answer

Named Entity Recognition (NER)

Answer

Sentiment Analysis

Answer

Topic Modeling

Answer

Text Summarization

Question 4

What is considered a primary limitation of the Bag-of-Words (BoW) model for text representation?

Accepted Answer

It disregards word order and semantic context.

Answer

It is computationally more expensive than deep learning models.

Answer

It can only be used for very small vocabularies.

Answer

It requires a labeled dataset for feature creation.

Question 5

A market research firm has collected thousands of open-ended survey responses about a new product. The goal is to discover the main themes or topics (e.g., 'pricing', 'features', 'customer support') discussed in the responses without any prior labeling. Which unsupervised NLP technique is most appropriate for this task?

Accepted Answer

Latent Dirichlet Allocation (LDA)

Answer

Text Classification

Answer

Part-of-Speech Tagging

Answer

Sentiment Analysis

Question 6

Which of the following is the primary advantage of using word embeddings (like Word2Vec) over a one-hot encoding representation?

Accepted Answer

They capture semantic relationships, placing words with similar meanings closer in the vector space.

Answer

They result in sparse, high-dimensional vectors that are easier for models to process.

Answer

They are simpler and faster to compute for any given vocabulary.

Answer

They perfectly represent every word in a language without any information loss.