A Generative Model for Semantic Role Labeling

By Cynthia A. Thompson, Roger Levy, Christopher D. Manning. 2003

91 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling (2017, background): “Though historically most SRL approaches did rely on syntax (Thompson et al., 2003; Pradhan et al., 2005; Punyakanok et al., 2008; Johansson and Nugues, 2008), the last generation of SRL models put syntax aside in favor of neural sequence models, namely LSTMs (Zhou and Xu, 2015; Marcheggiani et al.”
Semi-Supervised Semantic Role Labeling (2009, methodology): “For further details, see Thompson, Levy, & Manning (2003) .”
Frame-Semantic Parsing (2014, background): “Gildea and Jurafsky (2002) presented a discriminative model for arguments given the frame; Thompson, Levy, and Manning (2003) used a generative model for both the frame and its arguments.”
Classifying Semantic Relations in Bioscience Texts (2004, background): “…F D 7 (cid:8)&(cid:3)(cid:22)(cid:10)A,$((cid:22)’ F @)(cid:10)A,$((cid:22)’ F+G 71*+(cid:10)(cid:13)’9((cid:5)(cid:18) ; B CHD 7 (cid:8)&(cid:3)(cid:20)5 C F @)(cid:10)A,$((cid:22)’ F Model D1 is similar to the model in Thompson et al. (2003) for the extraction of roles, using a different domain.”
Tree Kernels for Semantic Role Labeling (2008, methodology): “For example, earlier studies on feature design for semantic role labeling were carried out by Gildea and Jurafsky (2002) and Thompson, Levy, and Manning (2003).”
Introduction to the CoNLL-2004 Shared Task: Semantic Role Labeling (2004, unknown): “…(Gildea and Juraf-sky, 2002; Gildea and Palmer, 2002; Gildea and Hock-enmaier, 2003), Maximum Entropy (Fleischman et al., 2003), generative models (Thompson et al., 2003), Decision Trees (Surdeanu et al., 2003; Chen and Ram-bow, 2003), and Support Vector Machines (Hacioglu and Ward, 2003; Pradhan…”
Special Issue Introduction: Semantic Role Labeling: An Introduction to the Special Issue (2008, unknown): “Finally, probabilistic models have also been applied to produce the structured output, for example, generative models (Thompson, Levy, and Manning 2003), sequence tagging with classiﬁers (M`arquez et al. 2005; Pradhan et al. 2005b), and Conditional Random Fields on tree structures (Cohn and Blunsom…”