A Joint Model for Semantic Role Labeling

By Aria Haghighi, Kristina Toutanova, Christopher D. Manning. 2005

63 citations recorded by the source corpus.

We present a semantic role labeling system submitted to the closed track of the CoNLL-2005 shared task. The system, introduced in (Toutanova et al., 2005), implements a joint model that captures dependencies among arguments of a predicate using log-linear models in a discriminative re-ranking framework. We also describe experiments aimed at increasing the robustness of the system in the presence of syntactic parse errors. Our final system achieves F1-Measures of 76.68 and 78.45 on the development and the WSJ portion of the test set, respectively.

Subfields: Artificial Intelligence, Natural Language Processing, Information Extraction

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Citations in the corpus

Natural Language Processing (Almost) from Scratch (2011, background): “State-ofthe-art systems often exploit additional parse trees such as the k top ranking parse trees (Koomen et al., 2005; Haghighi et al., 2005).”
Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling (2005, methodology): “Actually, Haghighi et al. (2005) performed a double selection step: an inner re-ranking of n -best solutions coming from the base system on a single tree; and an outer selection of the ﬁnal solution among the candidate solutions coming from n -best parse trees.”
Robust Parsing of the Proposition Bank (2006, methodology): “Accordingly, it is not possible to draw a straigthforward quantitative F R P (Haghighi et al., 2005) 83.4 83.1 83.7 (Pradhan et al., 2005) 83.3 83.0 83.5 (Punyakanok et al., 2005) 83.1 82.8 83.3 (Marquez et al., 2005) 83.1 82.8 83.3 (Surdeanu and Turmo, 2005) , 1999; Charniak, 2000), both for…”
Tree Kernels for Semantic Role Labeling (2008, methodology): “Furthermore, recent work (Haghighi, Toutanova, and Manning 2005; Punyakanok et al. 2005) has shown that, to achieve high labeling accuracy, joint inference should be applied on the whole predicate–argument structure.”
The Importance of Syntactic Parsing and Inference in Semantic Role Labeling (2008, methodology): “The top-4 systems, which produced significantly better results than the rest, all used some schemes to combine the output of several SRL systems, ranging from using a fixed combination function (Koomen et al., 2005; Haghighi et al., 2005) to using a machine learned combination strategy (Màrquez et a”