Summary of Exploring Spatial Representations in the Historical Lake District Texts with Llm-based Relation Extraction, by Erum Haris et al.
Exploring Spatial Representations in the Historical Lake District Texts with LLM-based Relation Extraction
by Erum Haris, Anthony G. Cohn, John G. Stell
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a method for extracting spatial relationships from historical narratives using a generative pre-trained transformer model. The Corpus of the Lake District Writing serves as the dataset, which is analyzed to understand the spatial dimensions inherent in the texts. A large language model is applied to capture nuanced connections between entities and locations, presenting outcomes as semantic triples and visualizing them as a network. This study contributes to comprehending the English Lake District’s spatial tapestry and provides an approach for uncovering spatial relations within diverse historical contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to figure out how people described places in old stories about the English Lake District. They used a special kind of computer program to look at lots of texts and find patterns about where things were located. By doing this, they want to understand how these old stories helped create the idea of what the place was like. The results are shown as simple connections between things and places, making it easier to see how everything fits together. |
Keywords
» Artificial intelligence » Large language model » Transformer
