{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125538"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125538","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Annotation-free location mention mining from text corpora","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_has_math":false,"creators":["Liu, Tingcong"],"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":2024,"date_issued":"2024-06-27","date_published":"2024-06-27","updated_at":"2026-07-22T22:25:02Z","subjects":["Text Mining","Pre-trained Language Model"],"languages":["en","eng"],"rights":["Copyright 2024 Tingcong Liu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/125538","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":["Liu, Tingcong"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-06-27","2024-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["Text Mining","Pre-trained Language Model"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Tingcong Liu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/125538"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Tingcong Liu, accepted the attached license on 2024-06-26 at 11:09.","The student, Tingcong Liu, submitted this Thesis for approval on 2024-06-26 at 11:24.","This Thesis was approved for publication on 2024-06-27 at 12:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20874 on 2025-02-04 at 21:03:56","This thesis provides a novel framework to extract location mentions from text corpora. Location mention mining plays an important role at analyzing and extracting structured knowledge from real-world text corpora like news and social media. Existing methods mainly rely on NER models or semantic parsers to extract locations but suffer from the following problems: (a) Entities tagged by NER models as LOC or GPE may not represent locations in the context. For example, in the sentence S1: “Ukraine forces are approaching Russia-held Kherson”, only “Kherson” is the true location mention although “Ukraine” and “Russia” are also of GPE type; and (b) A semantic parser cannot recognize locations in verb phrases. In S1, although “Kherson” refers to a location, it cannot be extracted as a locative argument by a semantic parser because it does not follow a preposition. This thesis defines a new task, location mention mining, aiming at extracting from a corpus all the mentions corresponding to real-world locations based on the context, and propose an annotation-free method, LocMine, which (1) constructs location-indicative term repositories using a background corpus and a knowledge base, (2) extracts and mines context-free location mentions based on the repositories, and (3) classifies context-dependent location mentions with pre-trained language models. This thesis provides extensive experiments and case studies showing that LocMine achieves the best performance among all the compared methods in terms of the ability to mine a complete set of location mentions from real-world corpora."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Annotation-free location mention mining from text corpora"]}]}],"canonical_facts":{"dc:contributor":["Han, Jiawei"],"dc:creator":["Liu, Tingcong"],"dc:date":["2024-06-27","2024-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Tingcong Liu, accepted the attached license on 2024-06-26 at 11:09.","The student, Tingcong Liu, submitted this Thesis for approval on 2024-06-26 at 11:24.","This Thesis was approved for publication on 2024-06-27 at 12:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20874 on 2025-02-04 at 21:03:56","This thesis provides a novel framework to extract location mentions from text corpora. Location mention mining plays an important role at analyzing and extracting structured knowledge from real-world text corpora like news and social media. Existing methods mainly rely on NER models or semantic parsers to extract locations but suffer from the following problems: (a) Entities tagged by NER models as LOC or GPE may not represent locations in the context. For example, in the sentence S1: “Ukraine forces are approaching Russia-held Kherson”, only “Kherson” is the true location mention although “Ukraine” and “Russia” are also of GPE type; and (b) A semantic parser cannot recognize locations in verb phrases. In S1, although “Kherson” refers to a location, it cannot be extracted as a locative argument by a semantic parser because it does not follow a preposition. This thesis defines a new task, location mention mining, aiming at extracting from a corpus all the mentions corresponding to real-world locations based on the context, and propose an annotation-free method, LocMine, which (1) constructs location-indicative term repositories using a background corpus and a knowledge base, (2) extracts and mines context-free location mentions based on the repositories, and (3) classifies context-dependent location mentions with pre-trained language models. This thesis provides extensive experiments and case studies showing that LocMine achieves the best performance among all the compared methods in terms of the ability to mine a complete set of location mentions from real-world corpora."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125538"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Tingcong Liu"],"dc:subject":["Text Mining","Pre-trained Language Model"],"dc:title":["Annotation-free location mention mining from text corpora"],"dc:type":["text","Thesis"],"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":"2026-07-22T22:25:02Z"}