Shallow semantic parsing using Support Vector Machines

Shallow semantic parsing using Support Vector Machines (2004)

by Sameer Pradhan , Wayne Ward , Kadri Hacioglu , James H. Martin

Citations:

109 - 4 self

Datum	Value	Source
TITLE	Shallow semantic parsing using Support Vector Machines	INFERENCE
AUTHOR NAME	Sameer Pradhan	SVM HeaderParse 0.2
AUTHOR AFFIL	Center for Spoken Language Research,	SVM HeaderParse 0.2
AUTHOR ADDR	University of Colorado, Boulder, CO 80303	SVM HeaderParse 0.2
AUTHOR NAME	Wayne Ward	SVM HeaderParse 0.2
AUTHOR AFFIL	Center for Spoken Language Research,	SVM HeaderParse 0.2
AUTHOR ADDR	University of Colorado, Boulder, CO 80303	SVM HeaderParse 0.2
AUTHOR NAME	Kadri Hacioglu	SVM HeaderParse 0.2
AUTHOR AFFIL	Center for Spoken Language Research,	SVM HeaderParse 0.2
AUTHOR ADDR	University of Colorado, Boulder, CO 80303	SVM HeaderParse 0.2
AUTHOR NAME	James H. Martin	SVM HeaderParse 0.2
AUTHOR AFFIL	Center for Spoken Language Research,	SVM HeaderParse 0.2
AUTHOR ADDR	University of Colorado, Boulder, CO 80303	SVM HeaderParse 0.2
ABSTRACT	In this paper, we propose a machine learning algorithm for shallow semantic parsing, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our algorithm is based on Support Vector Machines which we show give an improvement in performance over earlier classifiers. We show performance improvements through a number of new features and measure their ability to generalize to a new test set drawn from the AQUAINT corpus. 1	SVM HeaderParse 0.2
YEAR	2004	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	24 found	ParsCit 1.0