Mordecai 3: A Neural Geoparser and Event Geocoder
This is an incremental update to a widely used text geoparser, addressing the problem of geolocating text data for users in fields like geography and data analysis.
The paper introduces Mordecai 3, an end-to-end neural geoparser and event geocoder that resolves place names to a gazetteer and links events to locations using a question-answering model, with performance comparisons to existing geoparsers.
Mordecai3 is a new end-to-end text geoparser and event geolocation system. The system performs toponym resolution using a new neural ranking model to resolve a place name extracted from a document to its entry in the Geonames gazetteer. It also performs event geocoding, the process of linking events reported in text with the place names where they are reported to occur, using an off-the-shelf question-answering model. The toponym resolution model is trained on a diverse set of existing training data, along with several thousand newly annotated examples. The paper describes the model, its training process, and performance comparisons with existing geoparsers. The system is available as an open source Python library, Mordecai 3, and replaces an earlier geoparser, Mordecai v2, one of the most widely used text geoparsers (Halterman 2017).