The Necessity of Syntactic Parsing for Semantic Role Labeling

By Vasin Punyakanok, Dan Roth, Wen-tau Yih. 2005

135 citations recorded by the source corpus.

Subfields: Information Extraction, Computational Linguistics & Semantics

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In Computational Linguistics & Semantics

Citations in the corpus

Natural Language Processing (Almost) from Scratch (2011, background): “State-of-the-art SRL systems exploit parse trees (Gildea and Palmer, 2002; Punyakanok et al., 2005), related to CHUNK tags, and built using POS tags (Charniak, 2000; Collins, 1999).”
Search-based structured prediction (2009, neutral): “This has not thus far hindered its applicability to problems like sequence labeling [43], parsing and semantic role labeling [44], but does seem to be an overly strict condition.”
Deep RNNs Encode Soft Hierarchical Syntax (2018, background): “However, previous work has shown that syntactic information (in the form of either input features or supervision) is useful for a wide variety of NLP tasks (Punyakanok et al., 2005; Chiang et al., 2009), even in the neural setting (Aharoni and Goldberg, 2017; Chen et al., 2017).”
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs (2009, background): “To minimize this error, there has been significant interest in joint inference over multiple steps of an information processing pipeline [21, 22, 23].”
CogCompNLP: Your Swiss Army Knife for NLP (2018, methodology): “While missing a few modules (OpenIE and Constituency parsing), COGCOMPNLP contains high-level annotators for Semantic Role Labeling of verbs, nouns, commas, and prepositions, while also offering a much wider range of supporting functionality.”
Structured learning with constrained conditional models (2012, methodology): “…level (first order logic) description of the constraints was compiled into a set of linear inequalities, and exact inference was done using an integer linear programming formulation (ILP) (Roth and Yih 2004, 2005, 2007; Punyakanok et al. 2005a; Barzilay and Lapata 2006; Clarke and Lapata 2006).”
Joint Inference in Information Extraction (2007, background): “In natural language processing, joint inference has become a topic of keen interest (e.g., Sutton et al. (2006), Punyakanok et al. (2005)).”