{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90677"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90677","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Improving a supervised CCG parser","abstract":"The central topic of this thesis is the task of syntactic parsing with Combinatory Categorial Grammar (CCG). We focus on pipeline approaches that have allowed researchers to develop efficient and accurate parsers trained on articles taken from the Wall Street Journal (WSJ). We present three approaches to improving the state-of-the-art in CCG parsing. First, we test novel supertagger-parser combinations to identify the parsing models and algorithms that benefit the most from recent gains in supertagger accuracy. Second, we attempt to lessen the future burdens of assembling a state-of-the-art CCG parsing pipeline by showing that a part-of-speech (POS) tagger is not required to achieve optimal performance. Finally, we discuss the deficiencies of current parsing algorithms and propose a solution that promises improvements in accuracy – particularly for difficult dependencies – while preserving efficiency and optimality guarantees.","abstract_html":"The central topic of this thesis is the task of syntactic parsing with Combinatory Categorial Grammar (CCG). We focus on pipeline approaches that have allowed researchers to develop efficient and accurate parsers trained on articles taken from the Wall Street Journal (WSJ). We present three approaches to improving the state-of-the-art in CCG parsing. First, we test novel supertagger-parser combinations to identify the parsing models and algorithms that benefit the most from recent gains in supertagger accuracy. Second, we attempt to lessen the future burdens of assembling a state-of-the-art CCG parsing pipeline by showing that a part-of-speech (POS) tagger is not required to achieve optimal performance. Finally, we discuss the deficiencies of current parsing algorithms and propose a solution that promises improvements in accuracy – particularly for difficult dependencies – while preserving efficiency and optimality guarantees.","abstract_has_math":false,"creators":["Musa, Ryan A"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Hockenmaier, Julia"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-05","date_published":"2016-05","updated_at":"2026-07-22T22:26:34Z","subjects":["Natural language processing","combinatory categorial grammar","parsing"],"languages":["en"],"rights":["Copyright 2016 Ryan Musa"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90677","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hockenmaier, Julia"]},{"key":"dc:creator","label":"Author","values":["Musa, Ryan A"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-05","2016-07-07T19:58:16Z","2016-04-27"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Natural language processing","combinatory categorial grammar","parsing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Ryan Musa"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90677"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The central topic of this thesis is the task of syntactic parsing with Combinatory Categorial Grammar (CCG). We focus on pipeline approaches that have allowed researchers to develop efficient and accurate parsers trained on articles taken from the Wall Street Journal (WSJ). We present three approaches to improving the state-of-the-art in CCG parsing. First, we test novel supertagger-parser combinations to identify the parsing models and algorithms that benefit the most from recent gains in supertagger accuracy. Second, we attempt to lessen the future burdens of assembling a state-of-the-art CCG parsing pipeline by showing that a part-of-speech (POS) tagger is not required to achieve optimal performance. Finally, we discuss the deficiencies of current parsing algorithms and propose a solution that promises improvements in accuracy – particularly for difficult dependencies – while preserving efficiency and optimality guarantees.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-07-07 without embargo terms","The student, Ryan Musa, accepted the attached license on 2016-04-27 at 10:12.","The student, Ryan Musa, submitted this Thesis for approval on 2016-04-27 at 10:49.","This Thesis was approved for publication on 2016-04-27 at 15:50.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9542 on 2016-07-07 at 13:33:40","Made available in DSpace on 2016-07-07T19:58:16Z (GMT). No. of bitstreams: 2 MUSA-THESIS-2016.pdf: 534091 bytes, checksum: e2fb4de2b706f8a7e3a95db97f21612d (MD5) LICENSE.txt: 4206 bytes, checksum: a3c7ace28338f6abbbbe94ce397823ee (MD5) Previous issue date: 2016-04-27"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Improving a supervised CCG parser"]}]}],"canonical_facts":{"dc:contributor":["Hockenmaier, Julia"],"dc:creator":["Musa, Ryan A"],"dc:date":["2016-05","2016-07-07T19:58:16Z","2016-04-27"],"dc:description":["The central topic of this thesis is the task of syntactic parsing with Combinatory Categorial Grammar (CCG). We focus on pipeline approaches that have allowed researchers to develop efficient and accurate parsers trained on articles taken from the Wall Street Journal (WSJ). We present three approaches to improving the state-of-the-art in CCG parsing. First, we test novel supertagger-parser combinations to identify the parsing models and algorithms that benefit the most from recent gains in supertagger accuracy. Second, we attempt to lessen the future burdens of assembling a state-of-the-art CCG parsing pipeline by showing that a part-of-speech (POS) tagger is not required to achieve optimal performance. Finally, we discuss the deficiencies of current parsing algorithms and propose a solution that promises improvements in accuracy – particularly for difficult dependencies – while preserving efficiency and optimality guarantees.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-07-07 without embargo terms","The student, Ryan Musa, accepted the attached license on 2016-04-27 at 10:12.","The student, Ryan Musa, submitted this Thesis for approval on 2016-04-27 at 10:49.","This Thesis was approved for publication on 2016-04-27 at 15:50.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9542 on 2016-07-07 at 13:33:40","Made available in DSpace on 2016-07-07T19:58:16Z (GMT). No. of bitstreams: 2 MUSA-THESIS-2016.pdf: 534091 bytes, checksum: e2fb4de2b706f8a7e3a95db97f21612d (MD5) LICENSE.txt: 4206 bytes, checksum: a3c7ace28338f6abbbbe94ce397823ee (MD5) Previous issue date: 2016-04-27"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/90677"],"dc:language":["en"],"dc:rights":["Copyright 2016 Ryan Musa"],"dc:subject":["Natural language processing","combinatory categorial grammar","parsing"],"dc:title":["Improving a supervised CCG parser"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:34Z"}