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

Stemming is slower and more accurate than lemmatization

Answer

Lemmatization removes stop words; stemming does not

Answer

Both are identical processes with different names

Question 2

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

Accepted Answer

Organization name

Answer

Sentiment polarity

Answer

Part-of-speech tag

Answer

Token frequency

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

Ignore word frequency entirely

Answer

Require no training data to generate

Answer

Only work on numeric data

Question 4

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

Accepted Answer

F1-Score

Answer

Accuracy

Answer

Mean Squared Error

Answer

R-squared

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

To visualize word frequency distributions across documents

Answer

To reduce feature dimensions before model training

Answer

To cluster similar documents into topic groups

Question 6

In information retrieval and text mining, precision is defined as:

Accepted Answer

The fraction of retrieved documents that are actually relevant

Answer

The fraction of relevant documents retrieved out of all documents in the corpus

Answer

The total number of relevant documents in the dataset

Answer

The harmonic mean of recall and F1-score

Question 7

Which text-mining approach uses a trained model to automatically assign predefined categories to incoming documents?

Accepted Answer

Text classification

Answer

Document clustering

Answer

Association rule mining

Answer

Sequence analysis