Podcast
Questions and Answers
What is the main motivation for using Conditional Random Fields over Hidden Markov Models in NLP applications?
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?
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?
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?
How is training typically done for Conditional Random Fields?
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What optimization problem arises during training of Conditional Random Fields?
What optimization problem arises during training of Conditional Random Fields?
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Name three popular approaches used for training Conditional Random Fields.
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?
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?
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?
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?
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?
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?
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.
