We propose a sequence-alignment based method for detecting and disambiguating coordinate conjunctions. In this method, averaged perceptron learning is used to adapt the substituti...
In this paper, a discrimination and robusmess oriented adaptive learning procedure is proposed to deal with the task of syntactic ambiguity resolution. Owing to the problem of ins...
Many approaches to learning classifiers for structured objects (e.g., shapes) use generative models in a Bayesian framework. However, state-of-the-art classifiers for vectorial d...
In this paper, we propose a novel unsupervised approach to query segmentation, an important task in Web search. We use a generative query model to recover a query's underlyin...
The stochastic discrimination (SD) theory considers learning as building models of uniform coverage over data distributions. Despite successful trials of the derived SD method in s...