Exploring Spatial Representations in the Historical Lake District Texts with LLM-based Relation Extraction

TL;DR

This paper uses a large language model to extract and visualize spatial relations from historical texts about the English Lake District, enhancing understanding of past landscapes through semantic triples and network visualization.

Contribution

It introduces a novel approach employing generative pre-trained transformers to extract spatial relations from historical narratives, providing a new method for spatial analysis in historical texts.

Findings

Semantic triples effectively capture spatial relations.

Network visualization reveals complex spatial connections.

Method enhances understanding of historical landscapes.

Abstract

Navigating historical narratives poses a challenge in unveiling the spatial intricacies of past landscapes. The proposed work addresses this challenge within the context of the English Lake District, employing the Corpus of the Lake District Writing. The method utilizes a generative pre-trained transformer model to extract spatial relations from the textual descriptions in the corpus. The study applies this large language model to understand the spatial dimensions inherent in historical narratives comprehensively. The outcomes are presented as semantic triples, capturing the nuanced connections between entities and locations, and visualized as a network, offering a graphical representation of the spatial narrative. The study contributes to a deeper comprehension of the English Lake District's spatial tapestry and provides an approach to uncovering spatial relations within diverse historical contexts.