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WorksheetsNatural Language Processing CT-3
Total questions: 20
Worksheet time: 40mins
Which, Among the Following Trees, is the Pictorial Identification of the Derivation?
The binary tree
The oct tree
The parse tree
None of the above
The Compiler Detects Which Kind of Errors?
Logical errors only
Grammatical errors only
Both grammatical and logical errors
Neither logical nor grammatical error
In Which of the following Derivations is the Right-Most Non-Terminal Symbol Replaced at Every Step?
Rightmost
Right look ahead
Right claim
Right non-terminal
Which of the following is the Most Basic Phase of Structured Grammar?
Context-free grammar
Regular grammar
Context-sensitive grammar
All of the above
Which Parser is Known as the Top-Down Parser?
An LALR parser
A LR parser
Operator precedence parser
Recursive descent parser
Lexical Analyzer in the Compiler is Used For:
Removing comments
Breaking the syntaxes in the set of tokens
Removing whitespace
All of the above
Which Tool is Used to Group Characters in Tokens in the Compiler?
Parser
Code optimizer
Scanner
Code generator
What is the primary goal of chart parsing?
To split sentences into words
To efficiently parse sentences by avoiding redundant computations
To generate random sentences
To calculate the frequency of words
What is the CYK (Cocke-Younger-Kasami) algorithm used for in chart parsing?
Parsing context-free grammars
Detecting ambiguous sentences
Learning grammar rules
Constructing dependency graphs
In chart parsing, a chart typically represents:
A weighted graph of sentence structures
A dictionary of possible grammar rules
A table storing intermediate parsing results
A hierarchical representation of syntax
In an Earley parser, what are dotted rules used for?
Representing ambiguous grammar rules
Tracking the current position in a grammar rule
Representing completed parse trees
Defining probabilistic weights
Which of the following best describes a probabilistic context-free grammar (PCFG)?
A grammar that assigns probabilities to each production rule to reflect their likelihood of occurrence.
A grammar that defines a set of rules with equal probabilities for all productions.
A grammar that is deterministic and cannot handle ambiguity in parsing.
A grammar that only generates terminal symbols without intermediate non-terminal symbols.
In a PCFG, how is the probability of a parse tree computed?
By summing the probabilities of all production rules in the grammar.
By multiplying the probabilities of the production rules used to derive the tree.
By taking the maximum probability of the production rules in the tree.
By dividing the total number of productions by the number of terminal symbols in the tree.
How are the probabilities in a PCFG typically estimated?
By using neural networks to predict rule probabilities
By assigning equal probabilities to all rules in the grammar.
By counting the frequency of rule applications in a treebank and normalizing them.
By manually assigning probabilities to production rules.
In dependency parsing, what is the role of a scoring function in a discriminative model?
To calculate the likelihood of input sentences.
To generate all possible parse trees.
To assign probabilities to grammatical rules.
To evaluate and rank candidate dependency graphs.
What is a common challenge in applying discriminative models to dependency parsing?
Handling noisy or unstructured text input
The inability to model conditional probabilities
Limited availability of generative grammar rules
Computing probabilities for every parse tree in a joint model
Which of the following machine learning techniques is commonly used in discriminative models for dependency parsing?
Naive Bayes Classifier
Principal Component Analysis (PCA)
Linear classifiers such as Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
What kind of features are typically used in discriminative models for dependency parsing?
Handcrafted and/or learned features such as word pairs, part-of-speech tags, and distance between words
Probabilistic rules derived from corpora
Context-free grammar rules
Only semantic features of words
Which of the following is a widely used treebank in NLP?
WordNet
BERT Treebank
TF-IDF Treebank
Penn Treebank
What is the primary purpose of a treebank in Natural Language Processing (NLP)?
To train neural networks for sentiment analysis.
To generate random text using pre-trained models.
To provide syntactic annotations for sentences.
To store word embeddings for vocabulary expansion.
