An Empirical Study of Smoothing Techniques for Language Modeling

An Empirical Study of Smoothing Techniques for Language Modeling (1998)

by Stanley F. Chen

Citations:

Datum	Value	Source
TITLE	An Empirical Study of Smoothing Techniques for Language Modeling	INFERENCE
AUTHOR NAME	Stanley F. Chen	SVM HeaderParse 0.2
AUTHOR AFFIL	Harvard University; Aiken Computation Laboratory	SVM HeaderParse 0.2
AUTHOR ADDR	33 Oxford St.; Cambridge, MA 02138	SVM HeaderParse 0.2
ABSTRACT	We present an extensive empirical comparison of several smoothing techniques in the domain of language modeling, including those described by Jelinek and Mercer (1980), Katz (1987), and Church and Gale (1991). We investigate for the first time how factors such as training data size, corpus (e.g., Brown versus Wall Street Journal), and n-gram order (bigram versus trigram) affect the relative performance of these methods, which we measure through the cross-entropy of test data. In addition, we introduce two novel smoothing techniques, one a variation of Jelinek-Mercer smoothing and one a very simple linear interpolation technique, both of which outperform existing methods. 1	SVM HeaderParse 0.2
YEAR	1998	INFERENCE
VENUE TYPE	TECHREPORT	INFERENCE
PAGES	310--318	INFERENCE
VOLUME	13	INFERENCE
TECH	Technical Report TR-10-98	INFERENCE
CITATIONS	21 found	ParsCit 1.0