The Application of P-Bar Theory in Transformation-Based Error-Driven Learning
Bryant Harold Walley · Aquila Digital Community (University of Southern Mississippi) · 2014
In P-bar Theory, Perkins et al. (2014) proposed a rule based method for determining the context of a partext (i.e., a part of a text document). In Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging Brill (1995) demonstrates a method of error-driven learning applied to individual words at the sentence level to determine the part of speech each word represents. We combine these two concepts providing a transformation-based error-driven learning algorithm to improve the results obtained from the static rules Perkins proposed and determine if the rule order prediction will provide additional metadata.