Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

GeoReasoner: Reasoning On Geospatially Grounded Context For Natural Language Understanding

Yibo Yan, Joey Lee

TL;DR

GeoReasoner tackles geospatially grounded language understanding by integrating linguistic context with geospatial information from databases. It leverages an LLM to generate location descriptions and encodes spatial relations as pseudo-sentences, trained with geospatial contrastive learning and masked language modeling to align anchor and neighbor representations. The method achieves state-of-the-art results on toponym recognition, toponym linking, and geo-entity typing, demonstrating robust generalization to unseen geospatial contexts. This approach advances geospatial natural language understanding and has potential to improve location-based services and geo-aware NLP applications.

Abstract

In human reading and communication, individuals tend to engage in geospatial reasoning, which involves recognizing geographic entities and making informed inferences about their interrelationships. To mimic such cognitive process, current methods either utilize conventional natural language understanding toolkits, or directly apply models pretrained on geo-related natural language corpora. However, these methods face two significant challenges: i) they do not generalize well to unseen geospatial scenarios, and ii) they overlook the importance of integrating geospatial context from geographical databases with linguistic information from the Internet. To handle these challenges, we propose GeoReasoner, a language model capable of reasoning on geospatially grounded natural language. Specifically, it first leverages Large Language Models (LLMs) to generate a comprehensive location description based on linguistic and geospatial information. It also encodes direction and distance information into spatial embedding via treating them as pseudo-sentences. Consequently, the model is trained on both anchor-level and neighbor-level inputs to learn geo-entity representation. Extensive experimental results demonstrate GeoReasoner's superiority in three tasks: toponym recognition, toponym linking, and geo-entity typing, compared to the state-of-the-art baselines.

GeoReasoner: Reasoning On Geospatially Grounded Context For Natural Language Understanding

TL;DR

GeoReasoner tackles geospatially grounded language understanding by integrating linguistic context with geospatial information from databases. It leverages an LLM to generate location descriptions and encodes spatial relations as pseudo-sentences, trained with geospatial contrastive learning and masked language modeling to align anchor and neighbor representations. The method achieves state-of-the-art results on toponym recognition, toponym linking, and geo-entity typing, demonstrating robust generalization to unseen geospatial contexts. This approach advances geospatial natural language understanding and has potential to improve location-based services and geo-aware NLP applications.

Abstract

In human reading and communication, individuals tend to engage in geospatial reasoning, which involves recognizing geographic entities and making informed inferences about their interrelationships. To mimic such cognitive process, current methods either utilize conventional natural language understanding toolkits, or directly apply models pretrained on geo-related natural language corpora. However, these methods face two significant challenges: i) they do not generalize well to unseen geospatial scenarios, and ii) they overlook the importance of integrating geospatial context from geographical databases with linguistic information from the Internet. To handle these challenges, we propose GeoReasoner, a language model capable of reasoning on geospatially grounded natural language. Specifically, it first leverages Large Language Models (LLMs) to generate a comprehensive location description based on linguistic and geospatial information. It also encodes direction and distance information into spatial embedding via treating them as pseudo-sentences. Consequently, the model is trained on both anchor-level and neighbor-level inputs to learn geo-entity representation. Extensive experimental results demonstrate GeoReasoner's superiority in three tasks: toponym recognition, toponym linking, and geo-entity typing, compared to the state-of-the-art baselines.
Paper Structure (18 sections, 1 equation, 2 figures, 4 tables)

This paper contains 18 sections, 1 equation, 2 figures, 4 tables.

Table of Contents

  1. INTRODUCTION
  2. METHODOLOGY
  3. Data Collection and Preprocessing
  4. LLM-assisted Location Description Summarization
  5. Geo-Entity Representation Pretraining
  6. Geospatial Downstream Tasks
  7. EXPERIMENT
  8. Experimental Setup
  9. Datasets
  10. Evaluation
  11. Baselines
  12. Overall Performance
  13. Toponym Recognition
  14. Geo-Entity Typing
  15. Toponym Linking
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Comparison between our LLM-assisted GeoReasoner framework and conventional paradigms for geospatial natural language understanding task.
  • Figure 2: Overall Framework of GeoReasoner.