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What are some typical features used in natural language processing?
What are some typical features used in natural language processing?
Word at the current position, previous words, next words, prefix or suffix of the current word, position in the sentence, lowercase, Capitalized, ALLCAPS, CamelCase, Numbers, M1xed, contains a hyphen, number, word shape
What is the feature function in Natural Language Processing?
What is the feature function in Natural Language Processing?
Feature functions are binary functions
What is the role of Maximum Entropy Markov Models (MEMM) in NLP?
What is the role of Maximum Entropy Markov Models (MEMM) in NLP?
MEMM is a log-linear model with feature weights that help in optimization by constraining the expectation of each feature for consistency
What is the Label Bias Problem in NLP?
What is the Label Bias Problem in NLP?
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What is the solution to the Label Bias Problem?
What is the solution to the Label Bias Problem?
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In the Hidden Markov Model, which probability do we optimize?
In the Hidden Markov Model, which probability do we optimize?
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How is the Maximum Entropy Model optimized to satisfy constraints?
How is the Maximum Entropy Model optimized to satisfy constraints?
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What do we optimize directly with Maximum Entropy Taggers?
What do we optimize directly with Maximum Entropy Taggers?
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Why is fitting the states to the observed sentence in Unsupervised Hidden Markov Models insufficient for most applications?
Why is fitting the states to the observed sentence in Unsupervised Hidden Markov Models insufficient for most applications?
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How do we handle rare and unknown words in the context of state transitions?
How do we handle rare and unknown words in the context of state transitions?
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What is one idea mentioned for handling rare words in state transitions?
What is one idea mentioned for handling rare words in state transitions?
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How can we infer the most likely type of an unknown word based on word similarity?
How can we infer the most likely type of an unknown word based on word similarity?
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