2026-09-22T19:22:34Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/125538 2025-02-06 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

application/pdf en text 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 Annotation-free location mention mining from text corpora Liu, Tingcong 2024-06-27 Han, Jiawei Text Mining Pre-trained Language Model eng 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. 2024-08 Thesis https://hdl.handle.net/2142/125538 Copyright 2024 Tingcong Liu Computer Science Computer Science University of Illinois at Urbana-Champaign M.S. Thesis