{"id":{"repo_id":"usm","oai_identifier":"oai:aquila.usm.edu:masters_theses-1075"},"canonical_url":"https://search.dev.ndltd.org/etd/usm/oai:aquila.usm.edu:masters_theses-1075","repository":{"repo_id":"usm","name":"University of Southern Mississippi","base_url":"https://aquila.usm.edu/do/oai/"},"display":{"title":"The Application of P-Bar Theory in Transformation-Based Error-Driven Learning","abstract":"<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>","abstract_html":"&lt;p&gt;In &lt;em&gt;P-bar Theory&lt;/em&gt;, Perkins et al. (2014) proposed a rule based method for determining the context of a partext (i.e., a part of a text document). &lt;/p&gt; &lt;p&gt;In &lt;em&gt;Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging &lt;/em&gt;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. &lt;/p&gt; &lt;p&gt;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. &lt;/p&gt;","abstract_has_math":false,"creators":["Walley, Bryant Harold"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Masters Thesis","degree_discipline":"Computing","degree_department":null,"school":null,"contributors":["Louise Perkins","Sumanth Yenduri","Joe Zhang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-12-01T08:00:00Z","date_published":"2014-12-01T08:00:00Z","updated_at":"2026-07-24T05:44:27Z","subjects":["P-bar","transformation-based error-driven learning","natural language processing","context dictionary","Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://aquila.usm.edu/masters_theses/59","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Louise Perkins","Sumanth Yenduri","Joe Zhang"]},{"key":"dc:creator","label":"Author","values":["Walley, Bryant Harold"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2014-01-01T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computing"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["P-bar","transformation-based error-driven learning","natural language processing","context dictionary","Computer Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://aquila.usm.edu/masters_theses/59"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["<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>"]},{"key":"dc:title","label":"Title","values":["The Application of P-Bar Theory in Transformation-Based Error-Driven Learning"]}]}],"canonical_facts":{"dc:contributor":["Louise Perkins","Sumanth Yenduri","Joe Zhang"],"dc:creator":["Walley, Bryant Harold"],"dc:date.available":["2014-01-01T08:00:00Z"],"dc:description.abstract":["<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>"],"dc:identifier":["https://aquila.usm.edu/masters_theses/59"],"dc:subject":["P-bar","transformation-based error-driven learning","natural language processing","context dictionary","Computer Sciences"],"dc:title":["The Application of P-Bar Theory in Transformation-Based Error-Driven Learning"],"thesis:degree_discipline":["Computing"],"thesis:degree_level":["Masters Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T05:44:27Z"}