Pipeline computation, in which a task is decomposed into several stages that are solved sequentially, is a common computational strategy in natural language processing. The key problem of this model is that it results in error accumulation and suffers from its inability to correct mistakes in previous stages. We develop a framework for decisions made via in pipeline models, which addresses these difficulties, and presents and evaluates it in the context of bottom up dependency parsing for English. We show improvements in the accuracy of the inferred trees relative to existing models. Interestingly, the proposed algorithm shines especially when evaluated globally, at a sentence level, where our results are significantly better than those of existing approaches.

@conference{ChangDoRo06a,
  author = {M. Chang and Q. Do and D. Roth},
  title = {Local Search for Bottom-Up Dependency Parsing},
  booktitle = {ACL},
  month = {7},
  year = {2006},
  url = " http://cogcomp.cs.illinois.edu/papers/ChangDoRo06a.pdf",
  funding = {ARDA,REFLEX},
  projects = {DP},
  comment = {Analysis of A pipeline model with applications to dependency parsing},
}