Year: 2007
Type: conference-paper
Abstract: This paper describes our system for the task of extracting frame semantic structures in SemEval--2007. The system architecture uses two types of learning models in each part of the task: Support Vector Machines (SVM) and Maximum Entropy (ME). Designed as a pipeline of classifiers, the semantic parsing system obtained competitive precision scores on the test data.
Authors: Cosmin Adrian Bejan, Chris Hathaway
Institution: The University of Texas at Dallas
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2076059330