The Application of P-Bar Theory in Transformation-Based Error-Driven Learning

<p>In <em>P-bar Theory</em>, Perkins et al. (2014) proposed a rule based method for determining the context of a partext (i.e., a part of a text document). </p> <p>In <em>Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging </em>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. </p> <p>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. </p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Masters Thesis

Discipline thesis:degree_discipline

Computing

Year dc:date.available

2014

Author dc:creator

• Walley, Bryant Harold

Contributors dc:contributor

• Louise Perkins
• Sumanth Yenduri
• Joe Zhang

• P-bar
• transformation-based error-driven learning
• natural language processing
• context dictionary
• Computer Sciences

Repository record dc:identifier

https://aquila.usm.edu/masters_theses/59

OAI identifier oai:identifier

oai:aquila.usm.edu:masters_theses-1075