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Questions and Answers
What is the main motivation for using Conditional Random Fields over Hidden Markov Models in NLP applications?
Co-occurrences of terms matter.
What type of model is a Maximum Entropy Markov Model and how does it differ from a Hidden Markov Model?
Conditional model, non-generative.
Why can normalization be ignored in inference for a Linear-Chain Conditional Random Field?
Normalization can be ignored in inference because of the Viterbi Algorithm.
How is training typically done for Conditional Random Fields?
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What optimization problem arises during training of Conditional Random Fields?
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Name three popular approaches used for training Conditional Random Fields.
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In the context of Conditional Random Fields, what does the term 'feature functions' refer to?
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Why is the normalization step in Linear-Chain Conditional Random Fields able to be ignored during inference?
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What is the fundamental difference between the objective functions optimized during training for Hidden Markov Models and Conditional Random Fields?
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In the context of Linear-Chain Conditional Random Fields, what does the Viterbi algorithm compute during inference?
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What type of optimization problem arises during training of Conditional Random Fields, according to the text?
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Which of the following is NOT a popular approach used for training Conditional Random Fields?
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Study Notes
Conditional Random Fields – Motivation
- Hidden Markov Model (HMM) is a directed graph with strong temporal order, where a joint model of and only interacts with.
- However, in NLP applications, co-occurrences of terms matter, which is not captured by HMM.
Maximum Entropy Markov Model
- It is a conditional model for, non-generative.
- It uses feature functions derived from.
Linear-Chain Conditional Random Field
- Normalization can be ignored in inference because of.
Viterbi Algorithm for Linear-Chain CRF
- Use Viterbi algorithm to find the path with maximum.
- Initial states: Recurrence: Best path by backtracking from the maximum.
- Inference is similar to before, using the Viterbi algorithm, at least if we are only interested in finding the maximum, not the true probability.
Training for CRF
- Supervised learning is done by setting the derivative of the (conditional) log-likelihood zero.
- The likelihood of, which is constant with respect to the true parameters, can be dropped.
- Summing over all training documents (and adding a regularization) results in a convex optimization problem.
- Popular approaches for numerical optimization include:
- Improved Iterative Scaling
- Conjugate gradient descent
- Limited Memory Quasi-Newton approaches
Conclusions
- Hidden Markov Models are a standard technique for processing sequences.
- Conditional Random Fields are a suitable alternative for NLP applications where co-occurrences of terms matter.
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Description
Explore the motivation behind using Conditional Random Fields in Natural Language Processing (NLP) applications, comparing it with Hidden Markov Model and Maximum Entropy Markov Model. Learn about Linear-Chain Conditional Random Fields and the Viterbi Algorithm for inference.
