WorksheetsViva Exp1 & Exp2
Total questions: 25
Worksheet time: 13mins
Which of the following is NOT a type of ambiguity in NLP?
Statistical Ambiguity
Semantic Ambiguity
Syntactic Ambiguity
Lexical Ambiguity
What is tokenization in the context of NLP?
The process of splitting text into smaller units
The analysis of sentence structure
The identification of named entities
The conversion of text to speech
Which method is commonly used for Part-of-Speech (POS) tagging?
Rule-Based Tagging
Statistical Tagging
All of the above
Machine Learning-Based Tagging
What is the purpose of syntax analysis in NLP?
To perform sentiment analysis
To check the grammatical structure of sentences
To extract named entities from text
To visualize data
What is the role of POS tagging in information retrieval?
To analyze sentiment
To improve the precision of search results
To generate summaries of texts
To translate languages
Which of the following is a technique used for text categorization?
Chunking
Tokenization
Syntax Analysis
Naïve Bayes
What is the main disadvantage of top-down parsing?
It does not create parse trees
It is slower than bottom-up parsing
It cannot handle ambiguity
It requires backtracking
Which of the following best describes the concept of a parse tree?
A type of machine learning algorithm
A graphical representation of a derivation
A method for tokenizing text
A statistical model for POS tagging
What is the purpose of chunking in NLP?
To visualize data
To perform sentiment analysis
To extract phrases from text
To analyze sentence structure
Which of the following is a common application of POS tagging?
All of the above
Sentiment analysis
Machine translation
Text summarization
What does the term 'anaphoric ambiguity' refer to?
Ambiguity arising from pronouns
Ambiguity in statistical models
Ambiguity due to sentence structure
Ambiguity due to word meanings
Which phase of NLP involves breaking down language input into tokens?
Morphological Processing
Syntax Analysis
Pragmatic Analysis
Semantic Analysis
What is the main goal of semantic analysis in NLP?
To check for grammatical correctness
To tokenize the text
To identify named entities
To derive the exact meaning from text
What is the primary function of named entity recognition in NLP?
To convert text into speech
To analyze the sentiment of a text
To identify and classify key information in text
To summarize large documents
Which of the following techniques is used for disambiguating word meanings in NLP?
Text Classification
Word Sense Disambiguation
Parsing
Tokenization
What is the significance of lemmatization in text processing?
To categorize text into predefined classes
To extract numerical data from text
To analyze the grammatical structure of sentences
To reduce words to their base or root form
Why is text preprocessing important in NLP?
It removes valuable insights
It makes the text harder to interpret
It reduces data quality
It cleans messy data and improves model accuracy
What is the risk of removing all stopwords during preprocessing?
It makes training faster
It increases vocabulary size
It can remove important words like 'not' and change meaning
It improves context understanding
What is Part-of-Speech (POS) tagging?
Removing unwanted characters from text
Splitting text into subwords
Assigning grammatical categories to each word
Detecting spam messages
What does Named Entity Recognition (NER) identify in text?
Spelling errors
Sentiment polarity
Named entities like people, dates, and locations
Text length
What is the purpose of vectorization in NLP?
Translate text into speech
Convert text into numerical format for machine learning
Compress text files
Detect named entities
Which method considers the importance of words across the entire corpus?
Bag of Words
Lemmatization
Word Embeddings
TF-IDF
Why are word embeddings preferred over BoW?
They reduce training time
They are easier to compute
They capture semantic meaning and word relationships
They do not require any preprocessing
What makes Naive Bayes 'naive'?
It ignores punctuation
It assumes words are dependent
It uses no training data
It assumes word occurrences are independent
What is a key advantage of Naive Bayes?
Requires large datasets
Performs poorly on text data
Fast and effective on small datasets
Always requires embeddings
