Combining dependency parsers using error rates

Abstract

In this paper, we present a method of improving dependency parsing accuracy by combining parsers using error rates. We use four parsers: MSTParser, MaltParser, TurboParser and MateParser, and the data of the analytical layer of the Prague Dependency Treebank. We parse data with each of the parsers and calculate error rates for several parameters such as POS of dependent tokens. These error rates are then used to determine weights of edges in an oriented graph created by merging all the parses of a sentence provided by the parsers. We find the maximum spanning tree in this graph (a dependency tree without cycles), and achieve a 1.3 % UAS/1.1 % LAS improvement compared to the best parser in our experiment.