Improving Geocoding by Incorporating Geographical Hierarchy and Attributes Into Transformers Networks
When
Final Ph.D. Examination Of Zeyu Zhang
Committee: Dr. Steven Bethard (Advisor)
Dr. Clayton Morrison
Dr. Mihai Surdeanu
Dr. Xuan Lu
Wednesday, September 13th 2023 12:00pm
Harvill 460
Abstract:
With the development of artificial intelligence, machines are
empowering all aspects of people's lives. However, there are
still many shortcomings in AI. For example, AI robots cannot
accurately determine the place names in articles like
humans. After all, there are too many places with the same
name in the world. Therefore, Geocoding, the task of
converting location mentions in text to structured spatial
data, has recently seen progress thanks to a variety of new
datasets, evaluation metrics, and machine-learning
algorithms.
In this dissertation, I present empirical studies to explore
four research questions: 1. Are classic information retrieval
techniques competitive with modern neural approaches for
toponym resolution? 2. Can transformer-based reranking
improve over a strong candidate retrieval baseline? 3. Which
kind of context is most effective for toponym resolution? 4.
Is it better to approach toponym resolution as an ontology
entry ranking paradigm or a geographic attribute prediction
paradigm? Based on these questions, this dissertation
contains four research projects, in which we first show that
leveraging the better candidate generation, transformer-
based reranking, and two-stage resolution can improve
toponym resolution performance, and then introduce a new
efficient paradigm for toponym resoluton, which achieves a
new state-of-the-art.