Grammatical Bigrams

Grammatical Bigrams (2001)

by Mark Paskin

Venue:	Advances in Neural Information Processing Systems 14
Citations:	17 - 0 self

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Datum	Value	Source
TITLE	Grammatical Bigrams	SVM HeaderParse 0.1
AUTHOR NAME	Mark Paskin	user correction - Legacy Corrections
AUTHOR AFFIL	Computer Science Division; University of California, Berkeley	user correction - Legacy Corrections
AUTHOR ADDR	Berkeley, CA 94720	user correction - Legacy Corrections
ABSTRACT	Unsupervised learning algorithms have been derived for several statistical models of English grammar, but their computational complexity makes applying them to large data sets intractable. This paper presents a probabilistic model of English grammar that is much simpler than conventional models, but which admits an efficient EM training algorithm. The model is based upon grammatical bigrams, i.e., syntactic relationships between pairs of words.	user correction - Legacy Corrections
YEAR	2001	user correction - Legacy Corrections
VENUE	Advances in Neural Information Processing Systems 14	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	13 found	ParsCit 1.0