---
title: "Simple Training of Dependency Parsers via Structured Boosting"
authors:
  - "Qin Iris Wang"
  - "Dekang Lin"
  - "Dale Schuurmans"
doi: ""
url: "https://ijcai.org/proceedings/2007/284"
pdf: "https://ijcai.org/proceedings/2007/0284.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "1756-1761"
track: ""
keywords:
---

# Simple Training of Dependency Parsers via Structured Boosting

Qin Iris Wang, Dekang Lin, Dale Schuurmans

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 1756-1761.

## Abstract

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 some other components.We apply our techniques to the problem of learning dependency parsers from annotated natural language corpora. By using logistic regression as an efficient base classifier (for predicting dependency links between word pairs), we are able to efficiently train a dependency parsing model, via structured boosting, that achieves state of the art results in English, and surpasses state of the art in Chinese.