Artificial Intelligence MCQ Questions - Text Mining
Text Mining MCQs : This section focuses on "Text Mining" in Artificial Intelligence. These Multiple Choice Questions (MCQ) should be practiced to improve the AI skills required for various interviews (campus interviews, walk-in interviews, company interviews), placements, entrance exams and other competitive examinations.
1. ___________ is the process of transforming unstructured text into a structured format to identify meaningful patterns and new insights.
A. Data mining
B. Text mining
C. File mining
D. Deep mining
View Answer
Ans : B
Explanation: Text mining, also known as text data mining, is the process of transforming unstructured text into a structured format to identify meaningful patterns and new insights.
2. In which database, data is a blend between structured and unstructured data formats?
A. Full-structured data
B. Partial-structured data
C. Semi-structured data
D. Uni-structured data
View Answer
Ans : C
Explanation: Text is a one of the most common data types within databases. Semi-structured data: As the name suggests, this data is a blend between structured and unstructured data formats.
3. The process of breaking out long-form text into sentences and words called?
A. Stem
B. Cluster
C. Bag
D. Tokens
View Answer
Ans : D
Explanation: Tokenization: This is the process of breaking out long-form text into sentences and words called tokens.
4. Text mining is being used by large media companies, to clarify information and to provide readers with greater search experiences,
A. TRUE
B. FALSE
C. Can be true or false
D. Can not say
View Answer
Ans : A
Explanation: True, Text mining is being used by large media companies, to clarify information and to provide readers with greater search experiences
5. Typical text mining tasks include?
A. text categorization
B. text clustering
C. entity relation modeling
D. All of the above
View Answer
Ans : D
Explanation: Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling
6. Which of the following technique is not a part of flexible text matching?
A. Soundex
B. Metaphone
C. Keyword Hashing
D. Edit Distance
View Answer
Ans : C
Explanation: Except Keyword Hashing all other are the techniques used in flexible string matching
7. What is the right order for a text classification model components?
A. Text cleaning -> Text annotation -> Gradient descent -> Model tuning -> Text to predictors
B. Text cleaning -> Text annotation -> Text to predictors -> Gradient descent -> Model tuning
C. Text cleaning -> Gradient descent -> Model tuning -> Text to predictors -> Text annotation
D. Text cleaning -> Text annotation -> Model tuning -> Text to predictors -> Gradient descent
View Answer
Ans : B
Explanation: A right text classification model contains – cleaning of text to remove noise, annotation to create more features, converting text-based features into predictors, learning a model using gradient descent and finally tuning a model.
8. What are the possible features of a text corpus?
A. Count of word in a document
B. Vector notation of word
C. Part of Speech Tag
D. All of the above
View Answer
Ans : D
Explanation: All of the above are the possible features of a text corpus.
9. What is the major difference between CRF (Conditional Random Field) and HMM (Hidden Markov Model)?
A. CRF is Generative whereas HMM is Discriminative model
B. Both CRF and HMM are Generative model
C. CRF is Generative whereas HMM is Discriminative model
D. Both CRF and HMM are Discriminative mode
View Answer
Ans : C
Explanation: The major difference between CRF (Conditional Random Field) and HMM (Hidden Markov Model) : CRF is Discriminative whereas HMM is Generative model
10. Stemming: This refers to the process of separating the prefixes and suffixes from words to derive the root word form and meaning.
A. TRUE
B. FALSE
C. Can be true or false
D. Can not say
View Answer
Ans : A
Explanation: True, Stemming: This refers to the process of separating the prefixes and suffixes from words to derive the root word form and meaning.
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