A Sequential Model for Multi-Class Classification. EMNLP ’01

A Sequential Model for Multi-Class Classification. EMNLP ’01 (2001)

by Yair Even-zohar , Dan Roth

Citations:

32 - 11 self

Datum	Value	Source
TITLE	A Sequential Model for Multi-Class Classification. EMNLP ’01	INFERENCE
AUTHOR NAME	Yair Even-zohar	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science; University of Illinois at Urbana-Champaign	SVM HeaderParse 0.2
AUTHOR NAME	Dan Roth	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science; University of Illinois at Urbana-Champaign	SVM HeaderParse 0.2
ABSTRACT	Many classification problems require decisions among a large number of competing classes. These tasks, however, are not handled well by general purpose learning methods and are usually addressed in an ad-hoc fashion. We suggest a general approach – a sequential learning model that utilizes classifiers to sequentially restrict the number of competing classes while maintaining, with high probability, the presence of the true outcome in the candidates set. Some theoretical and computational properties of the model are discussed and we argue that these are important in NLP-like domains. The advantages of the model are illustrated in an experiment in partof-speech tagging. 1	SVM HeaderParse 0.2
YEAR	2001	INFERENCE
CITATIONS	31 found	ParsCit 1.0