TAILIEUCHUNG - Báo cáo khoa học: "Integrating Graph-Based and Transition-Based Dependency Parsers"

Previous studies of data-driven dependency parsing have shown that the distribution of parsing errors are correlated with theoretical properties of the models used for learning and inference. In this paper, we show how these results can be exploited to improve parsing accuracy by integrating a graph-based and a transition-based model. | Integrating Graph-Based and Transition-Based Dependency Parsers Joakim Nivre Vaxjo University Uppsala University Computer Science Linguistics and Philology SE-35195 Vaxjo Se-75126 Uppsala nivre@ Ryan McDonald Google Inc. 76 Ninth Avenue New York NY 10011 ryanmcd@ Abstract Previous studies of data-driven dependency parsing have shown that the distribution of parsing errors are correlated with theoretical properties of the models used for learning and inference. In this paper we show how these results can be exploited to improve parsing accuracy by integrating a graph-based and a transition-based model. By letting one model generate features for the other we consistently improve accuracy for both models resulting in a significant improvement of the state of the art when evaluated on data sets from the CoNLL-X shared task. 1 Introduction Syntactic dependency graphs have recently gained a wide interest in the natural language processing community and have been used for many problems ranging from machine translation Ding and Palmer 2004 to ontology construction Snow et al. 2005 . A dependency graph for a sentence represents each word and its syntactic dependents through labeled directed arcs as shown in figure 1. One advantage of this representation is that it extends naturally to discontinuous constructions which arise due to long distance dependencies or in languages where syntactic structure is encoded in morphology rather than in word order. This is undoubtedly one of the reasons for the emergence of dependency parsers for a wide range of languages. Many of these parsers are based on data-driven parsing models which learn to produce dependency graphs for sentences solely from an annotated corpus and can be easily ported to any Figure 1 Dependency graph for an English sentence. language or domain in which annotated resources exist. Practically all data-driven models that have been proposed for dependency parsing in recent years can be described as

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