This paper proposes a novel framework for automatic text categorization problem based on the kernel density classifier. The overall goal is to tackle two main issues in automatic ...
Dwi Sianto Mansjur, Ted S. Wada, Biing-Hwang Juang
This paper presents an architecture for solving generically the problem of extracting the constraints of a given task in a programming by demonstration framework and the problem...
Document representation and indexing is a key problem for document analysis and processing, such as clustering, classification and retrieval. Conventionally, Latent Semantic Index...
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 ...
We propose a novel bilingual topical admixture (BiTAM) formalism for word alignment in statistical machine translation. Under this formalism, the parallel sentence-pairs within a ...