Year: 2024
Type: dissertation
Abstract: 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 S 1 : "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 S 1 , 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 contextfree 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.
Author: Liu, Tingcong
Institution: University of Illinois Urbana-Champaign
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7220800374