Toponym Resolution
8 papers with code • 0 benchmarks • 1 datasets
The goal is to find a mapping from a toponym (a location mention) in the text to a spatial footprint.
Benchmarks
These leaderboards are used to track progress in Toponym Resolution
Most implemented papers
A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching
We report its performance on candidate selection in the context of the downstream task of toponym resolution, both on existing datasets and on a new manually-annotated resource of nineteenth-century English OCR'd text.
A Coherent Unsupervised Model for Toponym Resolution
The evaluation shows that our method outperforms the unsupervised technique as well as Reuters OpenCalais and Google Cloud Natural Language API on all three corpora; also, our method shows a performance close to that of the state-of-the-art supervised method and outperforms it when the test data has 40% or more toponyms that are not seen in the training data.
A Pragmatic Guide to Geoparsing Evaluation
Empirical methods in geoparsing have thus far lacked a standard evaluation framework describing the task, metrics and data used to compare state-of-the-art systems.
Are We There Yet? Evaluating State-of-the-Art Neural Network based Geoparsers Using EUPEG as a Benchmarking Platform
In June 2019, a geoparsing competition, Toponym Resolution in Scientific Papers, was held as one of the SemEval 2019 tasks.
Spatial Language Representation with Multi-Level Geocoding
We present a multi-level geocoding model (MLG) that learns to associate texts to geographic locations.
How can voting mechanisms improve the robustness and generalizability of toponym disambiguation?
A vast amount of geographic information exists in natural language texts, such as tweets and news.
Mordecai 3: A Neural Geoparser and Event Geocoder
Mordecai3 is a new end-to-end text geoparser and event geolocation system.
Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolution
Geocoding is the task of converting location mentions in text into structured data that encodes the geospatial semantics.