Analyzing the Errors of Unsupervised Learning

We identify four types of errors that unsupervised induction systems make and study each one in turn. Our contributions include (1) using a meta-model to analyze the incorrect biases of a model in a systematic way, (2) providing an efficient and robust method of measuring distance between two parameter settings of a model, and (3) showing that local optima issues which typically plague EM can be somewhat alleviated by increasing the number of training examples. We conduct our analyses on three models: the HMM, the PCFG, and a simple dependency model.

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Percy Liang, Dan Klein
• Keywords: ACL 2008, Computational Linguistics, Local Optima Issues, Simple Dependency Model, Unsupervised Induction Systems