2005 JointParsingAndSRL

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Subject Headings: Joint Inference Algorithm, Supervised Syntactic Parsing Algorithm, Supervised SRL Algorithm.

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Abstract

A striking feature of human syntactic processing is that it is context-dependent, that is, it seems to take into account semantic information from the discourse context and world knowledge. In this paper, we attempt to use this insight to bridge the gap between SRL results from gold parses and from automatically-generated parses. To do this, we jointly perform parsing and semantic role labeling, using a probabilistic SRL system to rerank the results of a probabilistic parser. Our current results are negative, because a locally-trained SRL model can return inaccurate probability estimates.

1. Introduction

Although much effort has gone into developing statistical parsing models and they have improved steadily over the years, in many applications that use parse trees errors made by the parser are a major source of errors in the final output. A promising approach to this problem is to perform both parsing and the higher-level task in a single, joint probabilistic model. This not only allows uncertainty about the parser output to be carried upward, such as through an k-best list, but also allows information from higher-level processing to improve parsing. For example, Miller et al. (2000) showed that performing parsing and information extraction in a joint model improves performance on both tasks. In particular, one suspects that attachment decisions, which are both notoriously hard and extremely important for semantic analysis, could benefit greatly from input from higher-level semantic analysis.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2005 JointParsingAndSRLCharles SuttonJoint Parsing and Semantic Role Labelinghttp://www.cs.umass.edu/~mccallum/papers/jointsrl-conll05.pdf