Theory and Algorithms for Information Extraction and Classification in Textual Data Mining

Regular expressions can be used as patterns to extract features from semi-structured and narrative text [8]. For example, in police reports a suspect’s height might be recorded as “{CD} feet {CD} inches tall”, where {CD} is the part of speech tag for a numeric value. The result in [1] shows us that regular expressions could have higher performance than explicit expressions in some applications such as Posting Act Tagging. Although much work has been done in the field of information extraction, relatively little has focused on the automatic discovery of regular expressions. Therefore, my Ph.D. research will focus on the automatic generation of reduced regular expressions (RREs) (defined in [8]) used in Information Extraction (IE). The reduced regular expressions learned can be directly used to extract features from free text, or they can be used to fill in templates in Eric Brill’s Transformation-Based Learning (TBL) [2] frameworks. The original templates in TBL are explicit expressions, which are weaker than reduced regular expressions. I propose an innovative enhancement to TBL termed “Error-Driven Boolean-Logic-Rule-Based Learning” (BLogRBL) [9], which is strictly more powerful than TBL [2]. Similar to Brill’s method, rules are automatically derived from templates during learning. It differs from Brill’s technique in that rules take the form of complex expressions of combinational logic. Therefore, my final contribution in my PhD thesis will be a framework that combines regular expression discovery with BLogRBL. A necessary component of this research is a study of various biases inherent in the use of reduced regular expressions in IE. The purpose of this work is to determine the language biases, search biases, and overfitting biases in the RRE discovery and BLogRBL algorithms.