Enriching the Output of a Parser Using Memory-based Learning

Enriching the Output of a Parser Using Memory-Based Learning Valentin Jijkoun and Maarten de Rijke Informatics Institute, University of Amsterdam � jijkoun, mdr � @science.uva.nl Abstract We describe a method for enriching the output of a parser with information available in a corpus. The method is based on graph rewriting using memory- based learning, applied to dependency structures. This general framework allows us to accurately re- cover both grammatical and semantic information as well as non-local dependencies. It also facili- tates dependency-based evaluation of phrase struc- ture parsers. Our method is largely independent of the choice of parser and corpus, and shows state of the art performance. 1 Introduction We describe a method to automatically enrich the output of parsers with information that is present in existing treebanks but usually not produced by the parsers themselves. Our motivation is two-fold. First and most important, for applications requiring information extraction or semantic interpretation of text, it is desirable to have parsers produce gram- matically and semantically rich output. Second, to facilitate dependency-based comparison and evalu- ation of different parsers, their outputs may need to be transformed into specific rich dependency for- malisms. The method allows us to automatically trans- form the output of a parser into structures as they are annotated in a dependency treebank. For a phrase structure parser, we first convert the pro- duced phrase structures into dependency graphs in a straightforward way, and then apply a se- quence of graph transformations: changing depen- dency labels, adding new nodes, and adding new dependencies. A memory-based learner trained on a dependency corpus is used to detect which modifications should be performed. For a depen- dency corpus derived from the Penn Treebank and the parsers we considered, these transformations correspond to adding Penn functional tags (e.g., -SBJ, -TMP, -LOC), empty nodes (e.g., NP PRO) and non-local dependencies (controlled traces, WH- extraction, etc.). For these specific sub-tasks our method achieves state of the art performance. The evaluation of the transformed output of the parsers of Charniak (2000) and Collins (1999) gives 90% unlabelled and 84% labelled accuracy with respect to dependencies, when measured against a depen- dency corpus derived from the Penn Treebank. The paper is organized as follows. After provid- ing some background and motivation in Section 2, we give the general overview of our method in Sec- tion 3. In Sections 4 through 8, we describe all stages of the transformation process, providing eval- uation results and comparing our methods to earlier work. We discuss the results in Section 9. 2 Background and Motivation State of the art statistical parsers, e.g., parsers trained on the Penn Treebank, produce syntactic parse trees with bare phrase labels, such as NP, PP, S, although the training corpora are usually much richer and often contain additional grammatical and semantic information (distinguishing various modi- fiers, complements, subjects, objects, etc.), includ- ing non-local dependencies, i.e., relations between phrases not adjacent in the parse tree. While this in- formation may be explicitly annotated in a treebank, it …

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