GeoLM：赋能语言模型以实现地理空间基础化的语言理解
GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding
AI总结：
本文提出地理空间基础化语言模型 GeoLM，利用地理实体提及作为锚点，通过对比学习、掩码语言建模及空间坐标嵌入机制融合文本与地理数据库信息，在地名识别、链接、关系抽取等任务上有效提升了语言模型的地理空间理解能力。
摘要：
人类在阅读文章时会潜意识地进行地理空间推理。我们识别文本中的地名及其空间关系，并在脑海中将其与地球上的物理位置联系起来。尽管预训练语言模型能够利用语言上下文来模仿这一认知过程，但它们并未利用大型且广泛可用的地理数据库（例如 OpenStreetMap）中宝贵的地理空间信息。本文提出了 GeoLM，一种地理空间基础化的语言模型，旨在增强对自然语言中地理实体的理解。GeoLM 利用地理实体提及作为锚点，将文本语料库中的语言信息与从地理数据库中提取的地理空间信息连接起来。GeoLM 通过对比学习和掩码语言建模来连接这两类上下文。它还引入了空间坐标嵌入机制，以编码距离和方向关系，从而捕捉地理空间上下文。在实验中，我们证明了 GeoLM 在支持地名识别、地名链接、关系抽取和地理实体类型判定方面展现出有前景的能力，这些任务架起了自然语言处理与地理空间科学之间的桥梁。代码已在 https://github.com/knowledge-computing/geolm 公开发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
