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 this https URL

GeoLM 是一个地理信息语言模型，通过连接文本语料库中的语言信息和来自地理数据库的地理信息，利用对比学习和遮蔽语言建模的方法，结合空间坐标嵌入机制来捕捉地理空间上下文，从而提升自然语言中的地理实体的理解能力。实验证明 GeoLM 在地名识别、地名链接、关系提取和地理实体类型等方面具有良好的能力，弥合了自然语言处理和地理空间科学之间的差距。