Unsupervised Learning in Computational Linguistics: Discover Patterns in Text Data

Question 1

Which of the following is NOT an advantage of unsupervised learning algorithms in NLP?

Accepted Answer

Guaranteed improvement in supervised learning performance

Answer

Discovery of patterns and structures in unlabeled text data

Answer

Elimination of the need for manual annotation

Answer

Reduced computational complexity

Question 2

Which unsupervised learning algorithm is used to group similar data points into clusters?

Accepted Answer

K-means clustering

Answer

Principal Component Analysis (PCA)

Answer

Linear Discriminant Analysis (LDA)

Answer

Support Vector Machines (SVMs)

Question 3

Which of the following is NOT a metric used to evaluate the performance of clustering algorithms?

Accepted Answer

Precision-Recall curve

Answer

Silhouette coefficient

Answer

Calinski-Harabasz index

Answer

Davies-Bouldin index

Question 4

What is the main objective of dimensionality reduction in NLP?

Accepted Answer

To reduce the number of features in a dataset, making it more manageable for analysis

Answer

To improve the accuracy of classification models

Answer

To make data visualization easier

Answer

To identify hidden patterns in text

Question 5

Which of the following NLP tasks can be addressed effectively using unsupervised learning?

Accepted Answer

Topic modeling

Answer

Sentiment analysis

Answer

Machine translation

Answer

Named entity recognition

Question 6

Explain the key difference between hard and soft clustering in NLP.

Accepted Answer

Hard clustering assigns each data point to a single cluster, while soft clustering allows data points to belong to multiple clusters

Answer

Hard clustering is more suitable for numerical data, while soft clustering is more suitable for categorical data

Answer

Hard clustering is more computationally expensive than soft clustering

Answer

Soft clustering produces more interpretable results than hard clustering

Question 7

Revised Question: What is a key distinction between supervised and unsupervised learning in NLP?

Accepted Answer

Revised Option: Supervised learning requires labeled data, while unsupervised learning utilizes unlabeled data.

Answer

Supervised learning focuses on predicting outcomes, while unsupervised learning focuses on discovering patterns.

Question 8

Revised Question: Which of the following is a common use case for clustering algorithms in unsupervised learning?

Accepted Answer

Revised Option: Identifying topics within a vast corpus of documents.

Answer

Translating text from one language to another.

Question 9

Revised Question: What is the primary objective of dimensionality reduction in unsupervised learning?

Accepted Answer

Revised Option: Reducing the number of features in a dataset with numerous dimensions.

Answer

To improve the accuracy of a supervised learning model.

Question 10

Revised Question: What is a potential limitation of utilizing unsupervised learning algorithms?

Accepted Answer

Revised Option: They can be unstable and sensitive to the initialization parameters.

Answer

They require large amounts of data for effective performance.

Question 11

Revised Question: What is a primary advantage of unsupervised learning for text data?

Accepted Answer

Revised Option: Discovering hidden patterns and insights in data that lacks labels.

Answer

It is less computationally expensive than supervised learning.

Question 12

Revised Question: Which metric is commonly employed to evaluate the performance of unsupervised learning models?

Accepted Answer

Revised Option: Silhouette coefficient

Answer

Accuracy

Question 13

Revised Question: Which dimensionality reduction technique is commonly used for text data?

Accepted Answer

Revised Option: Principal Component Analysis (PCA)

Answer

Support Vector Machines (SVM)

Question 14

Revised Question: How can unsupervised learning be applied in natural language generation?

Accepted Answer

Revised Option: Generating creative text, such as stories or poems.

Answer

Translating text from one language to another.

Question 15

Revised Question: Which Python library is commonly utilized for implementing unsupervised learning algorithms?

Accepted Answer

Revised Option: scikit-learn

Answer

TensorFlow

Question 16

Which of the following is NOT a type of unsupervised learning algorithm used in natural language processing (NLP)?

Accepted Answer

Reinforcement learning

Answer

Clustering

Answer

Dimensionality reduction

Answer

Topic modeling

Question 17

What is the primary objective of clustering in NLP?

Accepted Answer

To identify and group text data with similar characteristics

Answer

To reduce the dimensionality of high-dimensional text data

Answer

To extract hidden patterns and relationships from text

Answer

To classify text data into predefined categories

Question 18

What is a key advantage of using unsupervised learning algorithms in NLP?

Accepted Answer

They leverage unlabeled data, which is often more readily available

Answer

They require less computational power and resources

Answer

They outperform supervised learning algorithms in all NLP tasks

Answer

They are simpler to implement and interpret

Question 19

Which of the following is an example of a practical application of unsupervised learning in NLP?

Accepted Answer

Uncovering hidden topics in a large text corpus

Answer

Translating text from one language to another

Answer

Predicting the sentiment of a given piece of text

Answer

Identifying named entities in text

Question 20

What is the key distinction between hierarchical and partitional clustering algorithms?

Accepted Answer

Hierarchical algorithms create a hierarchical structure, while partitional algorithms divide data into non-overlapping clusters

Answer

Partitioning algorithms are more computationally efficient than hierarchical algorithms

Answer

Hierarchical algorithms can handle data with varying shapes, unlike partitional algorithms

Answer

Partitioning algorithms are better suited for large datasets, while hierarchical algorithms are more effective for small datasets

Question 21

Which evaluation metric is commonly used to assess the effectiveness of clustering algorithms in NLP?

Accepted Answer

Purity

Answer

F1-score

Answer

Recall

Answer

Root mean squared error (RMSE)

Question 22

What is the significance of stop words in unsupervised learning applied to NLP?

Accepted Answer

Stop words are typically removed to improve model performance and reduce noise

Answer

Stop words are assigned higher weights to emphasize their importance

Answer

Stop words are used to create a unique vocabulary for the model

Answer

Stop words are not relevant to unsupervised learning in NLP

Question 23

Which clustering algorithm utilizes a distance measure to form optimal clusters?

Accepted Answer

K-means

Answer

Hierarchical

Answer

Density-based

Answer

Model-based

Question 24

Which technique is used to perform dimensionality reduction by identifying the principal components of the data?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Singular Value Decomposition (SVD)

Answer

Multidimensional Scaling (MDS)

Answer

T-distributed Stochastic Neighbor Embedding (t-SNE)

Question 25

Which metric is commonly used to evaluate the performance of clustering algorithms?

Accepted Answer

Silhouette coefficient

Answer

Accuracy

Answer

Precision

Answer

Recall

Question 26

Which application of unsupervised learning is used to identify topics in a collection of documents?

Accepted Answer

Topic modeling

Answer

Sentence segmentation

Answer

Word embedding

Answer

Machine translation

Question 27

Which evaluation metric is commonly used to assess the performance of clustering algorithms?

Accepted Answer

Silhouette Score

Answer

Accuracy

Answer

F1 Score

Answer

Precision

Question 28

In a text embedding, each word is represented as a:

Accepted Answer

Vector of numerical values

Answer

Sequence of characters

Answer

Binary string

Answer

Natural language description

Question 29

Which of the following is not a type of unsupervised learning algorithm commonly utilized in computational linguistics?

Accepted Answer

Supervised learning

Answer

Clustering

Answer

Dimensionality reduction

Answer

Natural language processing

Question 30

What is the fundamental distinction between agglomerative and divisive hierarchical clustering?

Accepted Answer

Agglomerative methods commence with individual clusters and merge them, while divisive methods commence with the entire dataset and subsequently divide it

Answer

Agglomerative methods are more computationally efficient than divisive methods

Answer

Divisive methods can yield more balanced clusters than agglomerative methods

Answer

Both agglomerative and divisive methods are deterministic

Question 31

Which of the following algorithms is used for dimensionality reduction in unsupervised learning?

Accepted Answer

Principal Component Analysis (PCA)

Answer

K-Means Clustering

Answer

Hidden Markov Models (HMMs)

Answer

Naive Bayes

Question 32

What is the fundamental goal of unsupervised learning algorithms?

Accepted Answer

To discover hidden patterns and structures within data

Answer

To classify data into predetermined categories

Answer

To generate synthetic data instances

Answer

To adjust model parameters based on labeled datasets

Question 33

Which of the following is NOT a commonly encountered challenge in unsupervised learning?

Accepted Answer

Exploratory data analysis

Answer

Determining suitable evaluation metrics

Answer

Preventing overfitting and underfitting

Answer

Interpreting the obtained results

Question 34

What is the primary objective of topic modeling in unsupervised learning?

Accepted Answer

To identify latent topics within a text corpus

Answer

To minimize the dimensionality of text data

Answer

To assign documents into pre-defined categories

Answer

To generate novel text content

Question 35

Which metric is commonly employed to assess the performance of clustering algorithms?

Accepted Answer

Silhouette score

Answer

Precision and recall

Answer

F1-score

Answer

Receiver Operating Characteristic (ROC) curve

Question 36

Which of the following is not a commonly used unsupervised learning algorithm for text data?

Accepted Answer

Logistic regression

Answer

Clustering

Answer

Dimensionality reduction

Answer

Deep autoencoders

Question 37

What is the primary objective of clustering in natural language processing (NLP) tasks?

Accepted Answer

Identifying groups of documents with similar characteristics

Answer

Reducing the dimensions of data for analysis

Answer

Classifying documents into predetermined categories

Answer

Performing sentiment analysis on text data

Question 38

How can unsupervised learning benefit sentiment analysis tasks in natural language processing?

Accepted Answer

Identifying sentiment patterns in unlabeled text data

Answer

Automatically assigning sentiment scores to documents

Answer

Performing topic modeling on text data

Answer

Generating new text data with specific sentiment

Question 39

What is the fundamental difference between hierarchical and partitional clustering algorithms?

Accepted Answer

Hierarchical algorithms create a tree-like structure, while partitional algorithms divide data into distinct groups

Answer

Partitional algorithms create a tree-like structure, while hierarchical algorithms divide data into distinct groups

Answer

Hierarchical algorithms are faster than partitional algorithms

Answer

Partitional algorithms are more accurate than hierarchical algorithms

Question 40

Which of the following is not a type of unsupervised machine learning model?

Accepted Answer

Discriminative models

Answer

Generative models

Answer

Reinforcement learning models

Answer

Clustering models

Question 41

Which of the following is NOT a common unsupervised learning technique for clustering?

Accepted Answer

Principal Component Analysis

Answer

K-means

Answer

Gaussian Mixture Models

Answer

Hierarchical Clustering

Question 42

What is the primary goal of dimensionality reduction techniques in unsupervised learning?

Accepted Answer

To reduce the number of features in high-dimensional data while preserving its important characteristics

Answer

To predict future outcomes

Answer

To find hidden structures in data

Question 43

Which of the following is NOT an advantage of unsupervised learning?

Accepted Answer

Provides labeled data

Answer

Requires minimal human intervention

Answer

Can be used for exploratory data analysis

Answer

Can discover hidden patterns

Question 44

Which unsupervised learning algorithm is most commonly used for dimensionality reduction in NLP?

Accepted Answer

Principal Component Analysis (PCA)

Answer

K-Means Clustering

Answer

Hierarchical Clustering

Answer

Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

Question 45

Which type of clustering algorithm assigns data points to clusters based on their distance from a central point called a centroid?

Accepted Answer

Centroid-based clustering

Answer

Density-based clustering

Answer

Hierarchical clustering

Answer

Spectral clustering

Question 46

Which of the following is a common evaluation measure for assessing the performance of clustering algorithms?

Accepted Answer

Silhouette coefficient

Answer

Recall

Answer

F1 score

Answer

Precision

Question 47

Which of the following is a key challenge in unsupervised learning?

Accepted Answer

Determining the optimal number of clusters

Answer

Missing data

Answer

Data overfitting

Answer

Label noise

Question 48

Which unsupervised learning algorithm can effectively identify outliers in data?

Accepted Answer

Isolation Forest

Answer

K-Means Clustering

Answer

DBSCAN

Question 49

Which of the following is a fundamental principle of unsupervised learning?

Accepted Answer

Data contains inherent patterns and structures that can be uncovered without labeled data

Answer

Data must be normally distributed

Answer

Data is assumed to be labeled

Answer

Model parameters are predefined

Question 50

Which of the following is a common technique for evaluating the performance of clustering algorithms?

Accepted Answer

Silhouette analysis

Answer

Receiver operating characteristic curve (ROC)

Answer

Precision-recall curve

Answer

F1 score