Improving data driven wordclass tagging by system combination

Improving data driven wordclass tagging by system combination (1998)

by Hans Van Halteren , Jakub Zavrel , Walter Daelemans

Citations:

58 - 8 self

User correction supplied by walter.daelemans

Datum	Value	Source
TITLE	Improving data driven wordclass tagging by system combination	INFERENCE
AUTHOR NAME	Hans Van Halteren	user correction
AUTHOR AFFIL	Dept. of Language and Speech; University of Nijmegen	user correction
AUTHOR ADDR	P.O. Box 9103; 6500 IID Nijmegen; The Netherlands	user correction
AUTHOR NAME	Jakub Zavrel	user correction
AUTHOR NAME	Walter Daelemans	user correction
ABSTRACT	In this paper we examine how the differences in modelling between different data driven systems performing the same NLP task can be exploited to yield a higher accuracy than the best indi-vidua \| system. We do this by means of an ex-periment involving the task of morpho-syntactic wordclass tagging. Four well-known tagger gen-erators (Hidden Markov Model, Memory-Based, Transformation Rules and Maximum Entropy)	SVM HeaderParse 0.2
YEAR	1998	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	491--497	INFERENCE
CITATIONS	7 found	ParsCit 1.0