This paper presents the Topic-Aspect Model (TAM), a Bayesian mixture model which jointly discovers topics and aspects. We broadly define an aspect of a document as a characteristi...
We present a novel paradigm for statistical machine translation (SMT), based on a joint modeling of word alignment and the topical aspects underlying bilingual document-pairs, via...
We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic po...
Word prediction can be used for enhancing the communication ability of persons with speech and language impairments. In this work, we explore two methods of adapting a language mo...
Keith Trnka, Debra Yarrington, Kathleen F. McCoy, ...
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 ...