Joint Parsing and Semantic Role Labeling

Published 1 January 2005

Charles Sutton, Andrew McCallum

Citations22

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.

Keywords

Computer Science

Computational LinguisticsAutomatic Labeling of Semantic Roles

1,647 Citations2002Daniel Gildea, Daniel Jurafsky

View PDF

Discriminative Reranking for Natural Language Parsing

443 Citations2000Michael J. Collins

Using predicate-argument structures for information extraction

370 Citations2003Mihai Surdeanu, Sanda M. Harabagiu +2 more

The experimental results prove the claim that accurate predicate-argument structures enable high quality IE results, and introduce a new way of automatically identifying predicate argument structures, which is central to the IE paradigm.

View PDF

Calibrating Features for Semantic Role Labeling

320 Citations2004Nianwen Xue, Martha Palmer

A critical look at the features used in the semantic role tagging literature is taken and it is shown that the information in the input, generally a syntactic parse tree, has yet to be fully exploited.

Computational LinguisticsIntricacies of Collins' Parsing Model

265 Citations2004Daniel M. Bikel

A large set of heretofore unpublished details Collins used in his parser are documents, such that, along with Collins' (1999) thesis, this article contains all information necessary to duplicate Collins' benchmark results.

View PDF

A novel use of statistical parsing to extract information from text

200 Citations2000S.L. Miller, Heidi Fox +2 more

A lexicalized, probabilistic context-free parser is adapted to information extraction and this new technique is evaluated on MUC-7 template elements and template relations.

Joint learning improves semantic role labeling

182 Citations2005Kristina Toutanova, Aria Haghighi +1 more

This work shows how to build a joint model of argument frames, incorporating novel features that model these interactions into discriminative log-linear models, achieving error reduction of 22% on all arguments and 32% on core arguments over a state-of-the art independent classifier for gold-standard parse trees on PropBank.

View PDF