Support Vector Learning for Semantic Argument Classification

By Sameer Pradhan, Kadri Hacioglu, Valerie Krugler, Wayne H. Ward, James H. Martin, Dan Jurafsky. Machine-mediated learning, 2005

297 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/a584e4b607e972783cea22daaaf1114ea94a8035

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In Artificial Intelligence

Citations in the corpus

CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes (2012, methodology): “Propositions We used ASSERT21 (Pradhan et al., 2005) to predict the propositional structure for English.”
Semantic Role Labelling with Tree Conditional Random Fields (2005, methodology): “These features are described in Pradhan et al. (2005).”
Semantic Role Labeling with Neural Network Factors (2015, methodology): “While they use non-standard evaluation, they report accuracy similar to the ASSERT system (Pradhan et al., 2005), to which we compare in Table 4.”
A General Purpose FrameNet-based Shallow Semantic Parser (2010, methodology): “We first used Polynomial Kernels over handcrafted, linguistically-motivated, “standard” SRL features (Gildea and Jurafsky, 2002; Pradhan et al., 2005; Xue and Palmer, 2004).”
Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling (2005, methodology): “Their results on the WSJ test equal the best results published so far on this task and datasets (Pradhan et al., 2005a), though they are not directly comparable due to a different setting in deﬁning arguments not perfectly matching the predicted parse constituents.”
Introduction to the CoNLL-2004 Shared Task: Semantic Role Labeling (2004, methodology): “Regarding the learning component of the systems, we ﬁnd pure probabilistic models (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 an”
High-Order Low-Rank Tensors for Semantic Role Labeling (2015, background): “…primarily extended this core set by including local context lexicalized patterns (e.g., n -grams), several extended representations of the path features, and some linguistically motivated syntactic patterns, as the syntactic frame (Surdeanu et al., 2003; Xue and Palmer, 2004; Pradhan et al., 2005).”
Special Issue Introduction: Semantic Role Labeling: An Introduction to the Special Issue (2008, unknown): “Parent and sibling constituents in the tree may also be codiﬁed with all the previous structural and lexical features (Pradhan et al. 2005a; Surdeanu et al. 2007).”
Efficient Inference in Large Conditional Random Fields (2006, background): “The observation features were taken from [12], which included syntactic paths, head words, syntactic category, etc.”
Towards Robust Linguistic Analysis using OntoNotes (2013, methodology): “We used ASSERT15 (Pradhan et al., 2005) to predict the propositional structure for English.”
Shallow Semantic Parsing Based on FrameNet, VerbNet and PropBank (2006, methodology): “Also, we considered some of the features designed by [ 20 ]: First and Last Word/POS in Constituent , Subcategorization, Head Word of Prepositional Phrasesand the Syntactic Framefeature from [22].”
CoNLL-2011 Shared Task: Modeling Unrestricted Coreference in OntoNotes (2011, methodology): “Propositions To predict propositional structure, ASSERT14 (Pradhan et al., 2005) was used, retrained also on all the training portion of the release 12http://bllip.cs.brown.edu/download/rerankingparserAug06.tar.gz 13The number of lemmas in Table 6 do not add up to this number because not all of…”
Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees (2006, result): “the one based on PTs, shows lower accuracy than the SST and linear kernels but higher than the ST kernel and (c) the results using all training data are comparable with those obtained in [22], i.”
The Importance of Syntactic Parsing and Inference in Semantic Role Labeling (2008, methodology): “(2005) (their earlier version appeared in Pradhan et al. [2003]), which reported the performance on several systems using different information sources and system architectures.”