{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/106203"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/106203","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Document expansion and language model re-estimation for information retrieval","abstract":"Document expansion is the process of augmenting the text of a document with text drawn from one or more other documents. The purpose of this expansion is to increase the size of the term sample from which document representations, such as language models, may be estimated. While document expansion has been shown to improve the effectiveness of ad-hoc document retrieval, our work differs from previous work in a variety of ways. We propose a consistent language modeling approach to document expansion of full length documents. We also explore the use of one or more external document collections as sources of data during the expansion process. Our proposed methods prove successful in improving retrieval effectiveness over baselines. We also acknowledge that existing document expansion work, including our own, has relied on intuitive assumptions about the mechanisms by which it achieves its effects. In this thesis, we quantify aspects of document language model change resulting from expansion. We investigate the relationships between these changes and the operations of our model. In doing so, we establish evidence to support prior intuitions; specifically, we find relationships between the quality of a document's representation, which is used to identify appropriate expansion documents, and the expansion model's success in accurately re-estimating a language model. Finally, recognizing the potential for further retrieval effectiveness improvement by means of selective application of our model, we investigate methods for automatically predicting whether or not to expand individual documents and, if so, which expansion collection may yield the optimal document representation. We find that, although the document expansion retrieval model has proven effective overall, accurate prediction concerning the expansion of a given document depends too heavily on predicting the document's relevance. These findings suggest limitations to any model that may seek to optimize scoring on a per-document basis.","abstract_html":"Document expansion is the process of augmenting the text of a document with text drawn from one or more other documents. The purpose of this expansion is to increase the size of the term sample from which document representations, such as language models, may be estimated. While document expansion has been shown to improve the effectiveness of ad-hoc document retrieval, our work differs from previous work in a variety of ways. We propose a consistent language modeling approach to document expansion of full length documents. We also explore the use of one or more external document collections as sources of data during the expansion process. Our proposed methods prove successful in improving retrieval effectiveness over baselines. We also acknowledge that existing document expansion work, including our own, has relied on intuitive assumptions about the mechanisms by which it achieves its effects. In this thesis, we quantify aspects of document language model change resulting from expansion. We investigate the relationships between these changes and the operations of our model. In doing so, we establish evidence to support prior intuitions; specifically, we find relationships between the quality of a document's representation, which is used to identify appropriate expansion documents, and the expansion model's success in accurately re-estimating a language model. Finally, recognizing the potential for further retrieval effectiveness improvement by means of selective application of our model, we investigate methods for automatically predicting whether or not to expand individual documents and, if so, which expansion collection may yield the optimal document representation. We find that, although the document expansion retrieval model has proven effective overall, accurate prediction concerning the expansion of a given document depends too heavily on predicting the document's relevance. These findings suggest limitations to any model that may seek to optimize scoring on a per-document basis.","abstract_has_math":false,"creators":["Sherman, Garrick"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Library & Information Science","degree_department":null,"school":null,"contributors":["Diesner, Jana","Downie, J. Stephen","Underwood, Ted","Arguello, Jaime"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-03-02T21:58:14Z","date_published":"2020-03-02T21:58:14Z","updated_at":"2026-07-22T22:24:45Z","subjects":["information retrieval","document expansion","language models"],"languages":["en"],"rights":["Copyright 2019 Garrick Sherman"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/106203","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Diesner, Jana","Downie, J. 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The purpose of this expansion is to increase the size of the term sample from which document representations, such as language models, may be estimated. While document expansion has been shown to improve the effectiveness of ad-hoc document retrieval, our work differs from previous work in a variety of ways. We propose a consistent language modeling approach to document expansion of full length documents. We also explore the use of one or more external document collections as sources of data during the expansion process. Our proposed methods prove successful in improving retrieval effectiveness over baselines. We also acknowledge that existing document expansion work, including our own, has relied on intuitive assumptions about the mechanisms by which it achieves its effects. In this thesis, we quantify aspects of document language model change resulting from expansion. We investigate the relationships between these changes and the operations of our model. In doing so, we establish evidence to support prior intuitions; specifically, we find relationships between the quality of a document's representation, which is used to identify appropriate expansion documents, and the expansion model's success in accurately re-estimating a language model. Finally, recognizing the potential for further retrieval effectiveness improvement by means of selective application of our model, we investigate methods for automatically predicting whether or not to expand individual documents and, if so, which expansion collection may yield the optimal document representation. We find that, although the document expansion retrieval model has proven effective overall, accurate prediction concerning the expansion of a given document depends too heavily on predicting the document's relevance. These findings suggest limitations to any model that may seek to optimize scoring on a per-document basis.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-02-28 without embargo terms","The student, Garrick Sherman, accepted the attached license on 2019-11-21 at 19:38.","The student, Garrick Sherman, submitted this Dissertation for approval on 2019-11-21 at 19:44.","This Dissertation was approved for publication on 2019-11-25 at 14:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14587 on 2020-02-28 at 17:13:57","Made available in DSpace on 2020-03-02T21:58:14Z (GMT). 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While document expansion has been shown to improve the effectiveness of ad-hoc document retrieval, our work differs from previous work in a variety of ways. We propose a consistent language modeling approach to document expansion of full length documents. We also explore the use of one or more external document collections as sources of data during the expansion process. Our proposed methods prove successful in improving retrieval effectiveness over baselines. We also acknowledge that existing document expansion work, including our own, has relied on intuitive assumptions about the mechanisms by which it achieves its effects. In this thesis, we quantify aspects of document language model change resulting from expansion. We investigate the relationships between these changes and the operations of our model. In doing so, we establish evidence to support prior intuitions; specifically, we find relationships between the quality of a document's representation, which is used to identify appropriate expansion documents, and the expansion model's success in accurately re-estimating a language model. Finally, recognizing the potential for further retrieval effectiveness improvement by means of selective application of our model, we investigate methods for automatically predicting whether or not to expand individual documents and, if so, which expansion collection may yield the optimal document representation. We find that, although the document expansion retrieval model has proven effective overall, accurate prediction concerning the expansion of a given document depends too heavily on predicting the document's relevance. These findings suggest limitations to any model that may seek to optimize scoring on a per-document basis.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-02-28 without embargo terms","The student, Garrick Sherman, accepted the attached license on 2019-11-21 at 19:38.","The student, Garrick Sherman, submitted this Dissertation for approval on 2019-11-21 at 19:44.","This Dissertation was approved for publication on 2019-11-25 at 14:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14587 on 2020-02-28 at 17:13:57","Made available in DSpace on 2020-03-02T21:58:14Z (GMT). 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