We describe a case study in tit(', application of symbolic machinc learning techniques for the discow;ry of linguistic rules and categories. A supervised rule induction algor...
Statistical language models estimate the probability of a word occurring in a given context. The most common language models rely on a discrete enumeration of predictive contexts ...
John Blitzer, Kilian Q. Weinberger, Lawrence K. Sa...
This paper describes the results of some experiments exploring statistical methods to infer syntactic categories from a raw corpus in an unsupervised fashion. It shares certain po...
Accurate unsupervised learning of phonemes of a language directly from speech is demonstrated via an algorithm for joint unsupervised learning of the topology and parameters of a ...
A variety of information extraction techniques rely on the fact that instances of the same relation are "distributionally similar," in that they tend to appear in simila...