{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105828"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105828","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Unsupervised query expansion for theme queries by exploration and fusion","abstract":"Theme queries are a subtype of informational queries that cover broader topics than those expressed explicitly in the query terms. For this type of query, it is natural to resort to query reformation techniques such as query expansion. While traditional query expansion may be used to improve the query, new query terms are ranked only once and no verification of quality of the new ranking is made. In comparison, we propose to address such queries with iterative unsupervised expansion: adaptive query exploration and rank fusion. In the lack of human labels, we combine multiple signals to help select the best expansion candidates and further use rank fusion to generate the final ranking and the new query with discretion. The two modules iteratively improve each other: the rank fusion results provide an estimated ranking improvement score to guide the query exploration module and the query exploration modules selects the most promising query variants to add to the fusion. Experiments on the NYT dataset and the Washington Post dataset confirm that our proposed method successfully improves upon baseline methods.","abstract_html":"Theme queries are a subtype of informational queries that cover broader topics than those expressed explicitly in the query terms. For this type of query, it is natural to resort to query reformation techniques such as query expansion. While traditional query expansion may be used to improve the query, new query terms are ranked only once and no verification of quality of the new ranking is made. In comparison, we propose to address such queries with iterative unsupervised expansion: adaptive query exploration and rank fusion. In the lack of human labels, we combine multiple signals to help select the best expansion candidates and further use rank fusion to generate the final ranking and the new query with discretion. The two modules iteratively improve each other: the rank fusion results provide an estimated ranking improvement score to guide the query exploration module and the query exploration modules selects the most promising query variants to add to the fusion. Experiments on the NYT dataset and the Washington Post dataset confirm that our proposed method successfully improves upon baseline methods.","abstract_has_math":false,"creators":["Li, Sha"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Han, Jiawei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-11-26T20:49:31Z","date_published":"2019-11-26T20:49:31Z","updated_at":"2026-07-22T22:24:45Z","subjects":["Information retrieval","query expansion","contextual bandit algorithm"],"languages":["en"],"rights":["Copyright 2019 Sha Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105828","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei"]},{"key":"dc:creator","label":"Author","values":["Li, Sha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-11-26T20:49:31Z","2021-11-27T10:15:30Z","2019-07-16","2019-08"]},{"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":["Information retrieval","query expansion","contextual bandit algorithm"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Sha Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105828"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Theme queries are a subtype of informational queries that cover broader topics than those expressed explicitly in the query terms. For this type of query, it is natural to resort to query reformation techniques such as query expansion. While traditional query expansion may be used to improve the query, new query terms are ranked only once and no verification of quality of the new ranking is made. In comparison, we propose to address such queries with iterative unsupervised expansion: adaptive query exploration and rank fusion. In the lack of human labels, we combine multiple signals to help select the best expansion candidates and further use rank fusion to generate the final ranking and the new query with discretion. The two modules iteratively improve each other: the rank fusion results provide an estimated ranking improvement score to guide the query exploration module and the query exploration modules selects the most promising query variants to add to the fusion. Experiments on the NYT dataset and the Washington Post dataset confirm that our proposed method successfully improves upon baseline methods.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-08-01","The student, Sha Li, accepted the attached license on 2019-07-15 at 17:04.","The student, Sha Li, submitted this Thesis for approval on 2019-07-15 at 17:10.","This Thesis was approved for publication on 2019-07-16 at 09:33.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14332 on 2019-11-26 at 13:06:01","Made available in DSpace on 2019-11-26T20:49:31Z (GMT). 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For this type of query, it is natural to resort to query reformation techniques such as query expansion. While traditional query expansion may be used to improve the query, new query terms are ranked only once and no verification of quality of the new ranking is made. In comparison, we propose to address such queries with iterative unsupervised expansion: adaptive query exploration and rank fusion. In the lack of human labels, we combine multiple signals to help select the best expansion candidates and further use rank fusion to generate the final ranking and the new query with discretion. The two modules iteratively improve each other: the rank fusion results provide an estimated ranking improvement score to guide the query exploration module and the query exploration modules selects the most promising query variants to add to the fusion. Experiments on the NYT dataset and the Washington Post dataset confirm that our proposed method successfully improves upon baseline methods.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-08-01","The student, Sha Li, accepted the attached license on 2019-07-15 at 17:04.","The student, Sha Li, submitted this Thesis for approval on 2019-07-15 at 17:10.","This Thesis was approved for publication on 2019-07-16 at 09:33.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14332 on 2019-11-26 at 13:06:01","Made available in DSpace on 2019-11-26T20:49:31Z (GMT). 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