Date of Award

12-2014

Degree Type

Masters Thesis

Degree Name

Master of Science (MS)

Department

Computing

Committee Chair

Louise Perkins

Committee Chair Department

Computing

Committee Member 2

Sumanth Yenduri

Committee Member 2 Department

Computing

Committee Member 3

Joe Zhang

Committee Member 3 Department

Computing

Abstract

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.

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