Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
Document representation and indexing is a key problem for document analysis and processing, such as clustering, classification and retrieval. Conventionally, Latent Semantic Index...
When students first learn programming, they often rely on a simple operational model of a program’s behavior to explain how particular features work. Because such models build o...
Distributional, corpus-based descriptions have frequently been applied to model aspects of word meaning. However, distributional models that use corpus data as their basis have on...
This paper treats nominal entity tagging as a six-way (five categories plus nonentity) classification problem and applies a smoothing maximum entropy (ME) model with a Gaussian pr...