Learning text analysis rules for domain-specific natural language processing

An enormous amount of knowledge is needed to infer the meaning of unrestricted natural language. The problem can be reduced to a manageable size by restricting attention to a specific domain, which is a corpus of texts together with a predefined set of concepts that are of interest to that domain. Two widely different domains are used to illustrate this domain-specific approach. One domain is a collection of Wall Street Journal articles in which the target concept is management succession events: identifying persons moving into corporate management positions or moving out. A second domain is a collection of hospital discharge summaries in which the target concepts are various classes of diagnosis or symptom. The goal of an information extraction system is to identify references to the concept of interest for a particular domain. A key knowledge source for this purpose is a set of text analysis rules based on the vocabulary, semantic classes, and writing style peculiar to the domain. This thesis presents CRYSTAL, an implemented system that automatically induces domain-specific text analysis rules from training examples. CRYSTAL learns rules that approach the performance of hand-coded rules, are robust in the face of noise and inadequate features, and require only a modest amount of training data. CRYSTAL belongs to a class of machine learning algorithms called covering algorithms, and presents a novel control strategy with time and space complexities that are independent of the number of features. CRYSTAL navigates efficiently through an extremely large space of possible rules. CRYSTAL also demonstrates that expressive rule representation is essential for high performance, robust text analysis rules. While simple rules are adequate to capture the most salient regularities in the training data, high performance can only be achieved when rules are expressive enough to reflect the subtlety and variability of unrestricted natural language