Automatic Labeling of Semantic Roles Qualifying Exam Proposal

By Daniel Gildea, Dan Jurafsky. 2000

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Extraction

Citations in the corpus

Efficient grounding of abstract spatial concepts for natural language interaction with robot platforms (2018, background): “Gildea and Jurafsky [31] present a formulation that assigns semantic roles (e.g., agent, message, topic) to linguistic constituents.”
Calibrating Features for Semantic Role Labeling (2004, methodology): “For comparison purposes we ignore the frameset information for now, following the practice of Gildea and Palmer (Gildea and Jurafsky, 2002; Pradhan et al., 2003) and others.”
Extracting implicit knowledge from text (2009, background): “Closely related are the tasks of Semantic Role Labelling (SRL) (Gildea and Jurafsky, 2002; Swier and Stevenson, 2004); deriving selectional preferences (Zernik, 1992; Resnik, 1993b; Clark and Weir, 1999); extracting partial predicate-argument structure (Abney, 1996); and mapping natural language int”
Extracting Opinions, Opinion Holders, and Topics Expressed in Online News Media Text (2006, methodology): “Modeling: We follow the statistical approaches for semantic role labeling (Gildea and Jurafsky, 2002; Fleischman et. al, 2003) which separate the task into two steps: identify candidates of frame elements (Step 1) and assign semantic roles for those candidates (Step 2).”
Learning Semantic Parsers: An Important but Under-Studied Problem (2004, background): “Research on empirical semantic-role analysis (Fillmoreet al. 2000; Gildea & Jurafsky 2002) comes close to studying learning for semantic parsing; however, this task only involves marking natural-language phrases as filling specific semantic roles in a given verb frame and does not provide detailed…”
Sentence Level Discourse Parsing using Syntactic and Lexical Information (2003, background): “For example, Gildea and Jurafsky (2002) present a semantic parser that optimistically assumes that has access to perfect semantic segments.”
The Importance of Syntactic Parsing and Inference in Semantic Role Labeling (2008, methodology): “The pioneering work on building an automatic semantic role labeler was proposed by Gildea and Jurafsky (2002). In their setting, semantic role labeling was treated as a tagging problem on each constituent in a parse tree, solved by a two-stage architecture consisting of an argument identifier and an”