Inspecting the structural biases of dependency parsing algorithms

Yoav Goldberg, Michael Elhadad

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


We propose the notion of a structural bias inherent in a parsing system with respect to the language it is aiming to parse. This structural bias characterizes the behaviour of a parsing system in terms of structures it tends to under- and over- produce. We propose a Boosting-based method for uncovering some of the structural bias inherent in parsing systems. We then apply our method to four English dependency parsers (an Arc-Eager and Arc-Standard transition-based parsers, and first- and second-order graph-based parsers). We show that all four parsers are biased with respect to the kind of annotation they are trained to parse. We present a detailed analysis of the biases that highlights specific differences and commonalities between the parsing systems, and improves our understanding of their strengths and weaknesses. © 2010 Association for Computational Linguistics.


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