Abstract

03000211. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Keywords: Machine learning, semi-supervised learning, coupled semi-supervised learning, This thesis argues that successful semi-supervised learning is improved by learning many functions at once in a coupled manner. Given knowledge about constraints between functions to be learned (e.g., f1(x) → ¬f2(x)), forcing the models that are learned to obey these constraints can yield a more constrained, and therefore easier, set of learning problems. We apply these ideas to bootstrap learning methods as well as semi-supervised logistic regression models, and show that considerable improvements are achieved in both settings. In experimental work, we focus on the problem of extracting factual knowledge from the web. This problem is an ideal case study for the general problems that we study because there is an abundance of unlabeled web page data available, and because thousands or millions of functions are discussed on the web. Chapter 3 focuses on coupling the semi-supervised learning of information extractors that extract information from free text using textual extraction patterns (e.g., “mayor of X ” and “Y star

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