Improvements to Dependency Parsing Using Automatic Simplification of Data

Abstract

In dependency parsing, much effort is devoted to the development of new methods of language modeling and better feature settings. Less attention is paid to actual linguistic data and how appropriate they are for automatic parsing: linguistic data can be too complex for a given parser, morphological tags may not reflect well syntactic properties of words, a detailed, complex annotation scheme may be ill suited for automatic parsing. In this paper, I present a study of this problem on the following case: automatic dependency parsing using the data of the Prague Dependency Treebank with two dependency parsers: MSTParser and MaltParser. I show that by means of small, reversible simplifications of the text and of the annotation, a considerable improvement of parsing accuracy can be achieved. In order to facilitate the task of language modeling performed by the parser, I reduce variability of lemmas and forms in the text. I modify the system of morphological annotation to adapt it better for parsing. Finally, the dependency annotation scheme is also partially modified. All such modifications are automatic and fully reversible: after the parsing is done, the original data and structures are automatically restored. With MaltParser, I achieve an 8.3% error rate reduction.

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