In this paper, we present a unified knowledge based approach for sense disambiguation and semantic role labeling. Our approach performs both tasks through a single algorithm that ...
Most supervised language processing systems show a significant drop-off in performance when they are tested on text that comes from a domain significantly different from the domai...
Parsing plays an important role in semantic role labeling (SRL) because most SRL systems infer semantic relations from 1-best parses. Therefore, parsing errors inevitably lead to ...
In this technical report, we propose the use of Lexicalized Tree-Adjoining Grammar (LTAG) formalism as an important additional source of features for the Semantic Role Labeling (S...
This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model...