We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) fo...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale ...
This paper presents an effective method for generating natural language sentences from their underlying meaning representations. The method is built on top of a hybrid tree repres...
This paper describes a new toolkit - SCARF - for doing speech recognition with segmental conditional random fields. It is designed to allow for the integration of numerous, possib...
Coarticulation is one of the important factors that makes automatic sign language recognition a hard problem. Unlike in speech recognition, coarticulation effects in sign language...
Conditional random fields (CRFs) have been quite successful in various machine learning tasks. However, as larger and larger data become acceptable for the current computational ma...