Simple Training of Dependency Parsers via Structured Boosting

13 years 6 months ago

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Recently, signiﬁcant progress has been made on learning structured predictors via coordinated training algorithms such as conditional random ﬁelds and maximum margin Markov networks. Unfortunately, these techniques are based on specialized training algorithms, are complex to implement, and expensive to run. We present a much simpler approach to training structured predictors by applying a boosting-like procedure to standard supervised training methods. The idea is to learn a local predictor using standard methods, such as logistic regression or support vector machines, but then achieve improved structured classiﬁcation by ”boosting” the inﬂuence of misclassiﬁed components after structured prediction, re-training the local predictor, and repeating. Further improvement in structured prediction accuracy can be achieved by incorporating ”dynamic” features—i.e. an extension whereby the features for one predicted component can depend on the predictions already made for s...

Qin Iris Wang, Dekang Lin, Dale Schuurmans

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