{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105243"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105243","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Automatically identifying people shot by police from media sources","abstract":"Despite several instances of societal attention and widespread protests, there is no database of police-involved fatal shootings. To this end, it is extremely important to develop a system that will monitor media reports of police use of force in nearly real time. In particular, my thesis leverages the recent developments in the field of text classification and event extraction to achieve this goal. In order to develop a database of police-involved fatal shootings, we propose a multiple layer structure. The first layer is a Boolean query to extract articles from the Solr database which stores articles scraped from the internet. We then show various comparisons on how text classification performs in this domain and show a comprehensive analysis of the errors such a system makes. Finally, we show our results on a number of event extraction systems and successfully conclude that using event extraction on top of text classification improves the task of victim name extraction.","abstract_html":"Despite several instances of societal attention and widespread protests, there is no database of police-involved fatal shootings. To this end, it is extremely important to develop a system that will monitor media reports of police use of force in nearly real time. In particular, my thesis leverages the recent developments in the field of text classification and event extraction to achieve this goal. In order to develop a database of police-involved fatal shootings, we propose a multiple layer structure. The first layer is a Boolean query to extract articles from the Solr database which stores articles scraped from the internet. We then show various comparisons on how text classification performs in this domain and show a comprehensive analysis of the errors such a system makes. Finally, we show our results on a number of event extraction systems and successfully conclude that using event extraction on top of text classification improves the task of victim name extraction.","abstract_has_math":false,"creators":["Satapathy, Sidhartha"],"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":2019,"date_issued":"2019-08-23T20:48:23Z","date_published":"2019-08-23T20:48:23Z","updated_at":"2026-07-22T22:24:44Z","subjects":["Natural Language Processing","Police Shooting","Artificial Intelligence","Deep Learning","Machine Learning","Text Classification","Event Extraction"],"languages":["en"],"rights":["Copyright 2019 Sidhartha Satapathy"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105243","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":["Satapathy, Sidhartha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:48:23Z","2021-08-24T09:15:10Z","2019-04-23","2019-05"]},{"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","Police Shooting","Artificial Intelligence","Deep Learning","Machine Learning","Text Classification","Event Extraction"]}]},{"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 Sidhartha Satapathy"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105243"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Despite several instances of societal attention and widespread protests, there is no database of police-involved fatal shootings. 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Finally, we show our results on a number of event extraction systems and successfully conclude that using event extraction on top of text classification improves the task of victim name extraction.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Sidhartha Satapathy, accepted the attached license on 2019-04-22 at 16:12.","The student, Sidhartha Satapathy, submitted this Thesis for approval on 2019-04-22 at 16:15.","This Thesis was approved for publication on 2019-04-23 at 15:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13823 on 2019-08-22 at 16:23:35","Made available in DSpace on 2019-08-23T20:48:23Z (GMT). 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To this end, it is extremely important to develop a system that will monitor media reports of police use of force in nearly real time. In particular, my thesis leverages the recent developments in the field of text classification and event extraction to achieve this goal. In order to develop a database of police-involved fatal shootings, we propose a multiple layer structure. The first layer is a Boolean query to extract articles from the Solr database which stores articles scraped from the internet. We then show various comparisons on how text classification performs in this domain and show a comprehensive analysis of the errors such a system makes. Finally, we show our results on a number of event extraction systems and successfully conclude that using event extraction on top of text classification improves the task of victim name extraction.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Sidhartha Satapathy, accepted the attached license on 2019-04-22 at 16:12.","The student, Sidhartha Satapathy, submitted this Thesis for approval on 2019-04-22 at 16:15.","This Thesis was approved for publication on 2019-04-23 at 15:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13823 on 2019-08-22 at 16:23:35","Made available in DSpace on 2019-08-23T20:48:23Z (GMT). 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