GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding
This work addresses the gap between natural language processing and geospatial sciences, offering a novel method for geospatially grounded language understanding, though it is incremental in combining existing techniques.
The paper tackles the problem of language models lacking geospatial reasoning by introducing GeoLM, which integrates geographical databases with text to improve understanding of geo-entities, achieving promising results in tasks like toponym recognition and linking.
Humans subconsciously engage in geospatial reasoning when reading articles. We recognize place names and their spatial relations in text and mentally associate them with their physical locations on Earth. Although pretrained language models can mimic this cognitive process using linguistic context, they do not utilize valuable geospatial information in large, widely available geographical databases, e.g., OpenStreetMap. This paper introduces GeoLM, a geospatially grounded language model that enhances the understanding of geo-entities in natural language. GeoLM leverages geo-entity mentions as anchors to connect linguistic information in text corpora with geospatial information extracted from geographical databases. GeoLM connects the two types of context through contrastive learning and masked language modeling. It also incorporates a spatial coordinate embedding mechanism to encode distance and direction relations to capture geospatial context. In the experiment, we demonstrate that GeoLM exhibits promising capabilities in supporting toponym recognition, toponym linking, relation extraction, and geo-entity typing, which bridge the gap between natural language processing and geospatial sciences. The code is publicly available at https://github.com/knowledge-computing/geolm.