Papers › GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding

GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding

23 Oct 2023arXiv:2310.14478archive 2025-07-28

Zekun Li, Wenxuan Zhou, Yao-Yi Chiang, Muhao Chen

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.

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find_ref_closest_match knowledge-computing/geolm/src/utils/find_closest.py official repository ran no licence file found · pointer only · 483c5387f9dc84fa · report
find_self_closest_match knowledge-computing/geolm/src/utils/find_closest.py official repository ran no licence file found · pointer only · e209dd4d34fbfc1b · report
get_bert_embedding knowledge-computing/geolm/src/utils/common_utils.py official repository ran no licence file found · pointer only · 4a7b3a6c2f6fab08 · report
get_geoname_features knowledge-computing/geolm/experiments/entity_linking/link_geonames.py official repository ran no licence file found · pointer only · 6d00a7557de88993 · report
get_nl_feature knowledge-computing/geolm/experiments/entity_linking/link_geonames.py official repository ran no licence file found · pointer only · 913999c02161eece · report
get_offset_mapping knowledge-computing/geolm/experiments/entity_linking/link_geonames.py official repository ran no licence file found · pointer only · bc4204f020925bdc · report
get_spatialbert_embedding knowledge-computing/geolm/src/utils/common_utils.py official repository ran no licence file found · pointer only · d065f9d1e3e79707 · report
load_spatial_bert_pretrained_weights knowledge-computing/geolm/src/utils/common_utils.py official repository ran no licence file found · pointer only · f4ca4e7649a98226 · report
revert_dict knowledge-computing/geolm/src/dataset_utils/const.py official repository ran no licence file found · pointer only · 1039c0eead864adc · report
sort_ref_closest_match knowledge-computing/geolm/src/utils/find_closest.py official repository ran no licence file found · pointer only · cdea0bc961051adc · report
get_baseline_model knowledge-computing/geolm/src/utils/baseline_utils.py official repository unverified no licence file found · pointer only · 0e212eae08644f3a · report

Tasks

ArticlesContrastive LearningEntity TypingLanguage ModelingLanguage ModellingMasked Language ModelingRelation ExtractionToponym Recognition

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Contrastive Learning

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