Incorporating domain knowledge into the modeling process is an effective way
to improve learning accuracy. However, as it is provided by humans, domain
knowledge can only be specified with some degree of uncertainty. We propose to
explicitly model such uncertainty through probabilistic constraints over the
parameter space. In contrast to hard parameter constraints, our approach is
effective also when the domain knowledge is inaccurate and generally results in
superior modeling accuracy.

Maximum likelihood estimators are often of limited practical use due to the
intensive computation they require. We propose a family of alternative
estimators that maximize a stochastic variation of the composite likelihood
function. Each of the estimators resolve the computation-accuracy tradeoff
differently, and taken together they span a continuous spectrum of
computation-accuracy tradeoff resolutions. We prove the consistency of the
estimators, provide formulas for their asymptotic variance, statistical
robustness, and computational complexity.

Text documents are complex high dimensional objects. To effectively visualize
such data it is important to reduce its dimensionality and visualize the low
dimensional embedding as a 2-D or 3-D scatter plot. In this paper we explore
dimensionality reduction methods that draw upon domain knowledge in order to
achieve a better low dimensional embedding and visualization of documents. We
consider the use of geometries specified manually by an expert, geometries
derived automatically from corpus statistics, and geometries computed from
linguistic resources.