Multilingual dependency parsing: A pipeline approach
Ming‐Wei Chang, Quang Việt Ðỗ, Dan Roth · Amsterdam studies in the theory and history of linguistic science. Series 4, Current issues in linguistic theory · 2007
This paper develops a general framework for machine learning based dependency parsing based on a pipeline approach, where a task is decomposed into several sequential stages. To overcome the error accumulation problem of pipeline models, we propose two natural principles for pipeline frameworks: (i) make local decisions as reliable as possible, and (ii) reduce the number of sequential decisions made. We develop an algorithm that provably satisfies these principles and show that the proposed principles support several algorithmic choices that improve the dependency parsing accuracy significantly. We present state of the art experimental results for English and several other languages. 1 1