TAILIEUCHUNG - Báo cáo khoa học: "Semi-Supervised Semantic Role Labeling"

Large scale annotated corpora are prerequisite to developing high-performance semantic role labeling systems. Unfortunately, such corpora are expensive to produce, limited in size, and may not be representative. Our work aims to reduce the annotation effort involved in creating resources for semantic role labeling via semi-supervised learning. Our algorithm augments a small number of manually labeled instances with unlabeled examples whose roles are inferred automatically via annotation projection. We formulate the projection task as a generalization of the linear assignment problem | Semi-Supervised Semantic Role Labeling Hagen Furstenau Dept. of Computational Linguistics Saarland University Saarbrucken Germany hagenf@ Mirella Lapata School of Informatics University of Edinburgh Edinburgh UK mlap@ Abstract Large scale annotated corpora are prerequisite to developing high-performance semantic role labeling systems. Unfortunately such corpora are expensive to produce limited in size and may not be representative. Our work aims to reduce the annotation effort involved in creating resources for semantic role labeling via semi-supervised learning. Our algorithm augments a small number of manually labeled instances with unlabeled examples whose roles are inferred automatically via annotation projection. We formulate the projection task as a generalization of the linear assignment problem. We seek to find a role assignment in the unlabeled data such that the argument similarity between the labeled and unlabeled instances is maximized. Experimental results on semantic role labeling show that the automatic annotations produced by our method improve performance over using hand-labeled instances alone. 1 Introduction Recent years have seen a growing interest in the task of automatically identifying and labeling the semantic roles conveyed by sentential constituents Gildea and Jurafsky 2002 . This is partly due to its relevance for applications ranging from information extraction Surdeanu et al. 2003 Mos-chitti et al. 2003 to question answering Shen and Lapata 2007 paraphrase identification Pado and Erk 2005 and the modeling of textual entailment relations Tatu and Moldovan 2005 . Resources like FrameNet Fillmore et al. 2003 and PropBank Palmer et al. 2005 have also facilitated the development of semantic role labeling methods by providing high-quality annotations for use in train ing. Semantic role labelers are commonly developed using a supervised learning paradigm1 where a classifier learns to predict role labels based on

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