This paper studies the computational complexity of disambiguation under probabilistic tree-grammars as in (Bod, 1992; Schabes and Waters, 1993). It presents a proof that the follo...
This paper investigates adapting a lexicalized probabilistic context-free grammar (PCFG) to a novel domain, using maximum a posteriori (MAP) estimation. The MAP framework is gener...
Identifying background (context) information in scientific articles can help scholars understand major contributions in their research area more easily. In this paper, we propose ...
ABSTRACT. Canonical models are of central importance in modal logic, in particular as they witness strong completeness and hence compactness. While the canonical model construction...
In this paper, we develop multilingual supervised latent Dirichlet allocation (MLSLDA), a probabilistic generative model that allows insights gleaned from one language's data...