DMC Text Mining & Natural Language Processing 2

Question 1

What distinguishes lemmatization from stemming in NLP preprocessing?

Accepted Answer

Lemmatization uses dictionary lookup to return valid base words; stemming applies heuristic rules

Answer

Lemmatization uses morphological analysis and a dictionary to return the canonical base form, producing valid words, while stemming uses rule-based trimming that may yield non-words.

Question 2

In the context of Named Entity Recognition (NER), which of the following is a typical entity category?

Accepted Answer

Organization name

Answer

NER identifies and classifies real-world entities such as persons, organizations, and locations within text.

Question 3

Word embeddings like Word2Vec differ from bag-of-words representations primarily because they:

Accepted Answer

Capture semantic similarity through dense vector representations

Answer

Word2Vec trains neural networks to place semantically similar words close together in a dense vector space, capturing meaning that bag-of-words cannot.

Question 4

Which evaluation metric is most appropriate when measuring a text classifier's performance on an imbalanced dataset?

Accepted Answer

F1-Score

Answer

F1-Score balances precision and recall, making it suitable when class imbalance means accuracy can be misleadingly high.

Question 5

What is the primary purpose of a confusion matrix in text classification evaluation?

Accepted Answer

To show true/false positive and negative prediction counts per class

Answer

A confusion matrix tabulates correct and incorrect predictions for each class, revealing where a classifier makes errors.

DMC - Data Mining Certification Practice Test

DMC - Data Mining Certification Practice Test

DMC Text Mining & Natural Language Processing 2