We describe a method for training a semantic role labeler for CCG in the absence of gold-standard syntax derivations. Traditionally, semantic role labeling is performed by placing human-annotated semantic roles on gold-standard syntactic parses, identifying patterns in the syntaxsemantics relationship, and then predicting roles on novel syntactic analyses. The gold standard syntactic training data can be eliminated from the process by extracting training instances from semantic roles projected onto a packed parse chart. This process can be used to rapidly develop NLP tools for resource-poor languages of interest.
REF describe a method for training a semantic role labeler by extracting features from a packed CCG parse chart, where the parse weights are given by a simple ruleset.
Semantic Role Labeling Without Treebanks?
IJCNLP
Computer Science