Papers by Yao-Yi Chiang
GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding (2023.emnlp-main)
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| Challenge: | Pretrained language models do not utilize valuable geospatial information in large databases, e.g., OpenStreetMap. |
| Approach: | They propose a geospatially grounded language model that connects linguistic and geospheric contexts. |
| Outcome: | The proposed model bridges the gap between natural language processing and geospatial sciences. |
SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation (2022.findings-emnlp)
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| Challenge: | Named geographic entities are the building blocks of many geographic datasets. |
| Approach: | They propose a spatial language model that provides a general-purpose geo-entity representation based on neighboring entities in geospatial data. |
| Outcome: | The proposed model improves on two downstream tasks, showing significant performance improvement compared with existing models that do not use spatial context. |