Paper: Approximation Lasso Methods For Language Modeling

ACL ID P06-1029
Title Approximation Lasso Methods For Language Modeling
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

Lasso is a regularization method for pa- rameter estimation in linear models. It op- timizes the model parameters with respect to a loss function subject to model com- plexities. This paper explores the use of lasso for statistical language modeling for text input. Owing to the very large number of parameters, directly optimizing the pe- nalized lasso loss function is impossible. Therefore, we investigate two approxima- tion methods, the boosted lasso (BLasso) and the forward stagewise linear regres- sion (FSLR). Both methods, when used with the exponential loss function, bear strong resemblance to the boosting algo- rithm which has been used as a discrimi- native training method for language mod- eling. Evaluations on the task of Japanese text input show that BLasso is able to produce the...