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What is the main purpose of a maximum-entropy Markov model (MEMM)?
What is the main purpose of a maximum-entropy Markov model (MEMM)?
- To calculate the conditional probability of a sequence of labels given a sequence of observations (correct)
- To generate random sequences of observations
- To find the maximum entropy of a given set of observations
- To analyze the independence of different observations
Where do MEMMs find applications in natural language processing?
Where do MEMMs find applications in natural language processing?
- Speech recognition
- Part-of-speech tagging and information extraction (correct)
- Semantic analysis
- Machine translation
In what way does an MEMM extend a standard maximum entropy classifier?
In what way does an MEMM extend a standard maximum entropy classifier?
- By assuming that the unknown values to be learnt are connected in a Markov chain (correct)
- By introducing additional random variables into the model
- By assuming that the unknown values to be learnt are conditionally independent of each other
- By eliminating the need for transition probabilities
Where does each transition probability in an MEMM come from?
Where does each transition probability in an MEMM come from?
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How is the conditional probability P(S1, …, Sn | O1, …, On) factored in an MEMM?
How is the conditional probability P(S1, …, Sn | O1, …, On) factored in an MEMM?
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