NLP_W9_B1

What is the thematic role of the entity that performs the action in a sentence?

• Patient
  

• Beneficiary

• Theme

• Agent

In the sentence, "The teacher gave the book to the student," what is the thematic role of "the student"

• Agent
  

• Goal

• Beneficiary

• Theme

Which of the following sentences has a clear example of a "Location" thematic role

• John put the book on the shelf.
  

• He cut the paper with scissors.

• She made a cake for her mother.

• The book was given to the student.

What is the primary goal of PropBank annotation

• To identify coreference chains

• To assign semantic roles to arguments of verbs

• To determine word frequency

• To analyze sentence syntax

Which of the following is TRUE about PropBank annotation

• It labels arguments of verbs with semantic roles.

• It involves annotating the syntactic structure of sentences.

• It focuses on part-of-speech tagging.

• It is based on dependency structures.

Which component of a sentence is typically labeled as "Arg2" in PropBank

• Direction or endpoint of the action

• Instrument

• Manner

• Beneficiary

In a Markov Chain, the probabilities associated with moving from one state to another are known as:

• 
  Steady-state probabilities

• Initial state probabilities

• Transition probabilities

• Absorbing probabilities

What is a "steady-state" distribution in a Markov Chain

• The distribution of states for an absorbing Markov Chain

• The distribution of states with zero transitions

• The distribution of states after a large number of steps, where probabilities no longer change

• The distribution of states at the first time step

In a Markov Chain, if the system has a finite number of states and the transition matrix is stochastic, then:

• The system will eventually reach an absorbing state

• The system can never reach a steady state

• The row sums of the transition matrix must equal 1

• The system has an infinite number of possible states

Which of the following is an example of a Markov process

• A system with random transitions based on previous states and external factors

• A coin flip where the outcome depends on previous flips

• A weather model where tomorrow’s weather depends only on today’s weather

• Stock prices where tomorrow’s price depends on the entire past history