# Unsupervised Location Mapping for Narrative Corpora

arXiv: https://arxiv.org/abs/2504.05954 · HTML: https://mlsanity.com/paper/2504.05954/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies](https://mlsanity.com/paper/2308.03188/) (2023)
- [Unlimiformer: Long-Range Transformers with Unlimited Length Input](https://mlsanity.com/paper/2305.01625/) (2023)
- [Location reference identification from tweets during emergencies: A deep learning approach](https://mlsanity.com/paper/1901.08241/) (2019)

## Built on by (most cited first, 1 of 1)

- [The Shape of Testimony: A Scalable Framework for Oral History Archive Comparison](https://mlsanity.com/paper/2605.21623/) (2026)

## Abstract (arXiv)

This work presents the task of unsupervised location mapping, which seeks to map the trajectory of an individual narrative on a spatial map of locations in which a large set of narratives take place. Despite the fundamentality and generality of the task, very little work addressed the spatial mapping of narrative texts. The task consists of two parts: (1) inducing a ``map'' with the locations mentioned in a set of texts, and (2) extracting a trajectory from a single narrative and positioning it on the map. Following recent advances in increasing the context length of large language models, we propose a pipeline for this task in a completely unsupervised manner without predefining the set of labels. We test our method on two different domains: (1) Holocaust testimonies and (2) Lake District writing, namely multi-century literature on travels in the English Lake District. We perform both intrinsic and extrinsic evaluations for the task, with encouraging results, thereby setting a benchmark and evaluation practices for the task, as well as highlighting challenges.