# Cross-referencing using Fine-grained Topic Modeling

## TL;DR

This paper presents a cost-effective, topic-based system that automatically generates candidate cross-references in texts using fine-grained topic modeling, aiding comprehension and reducing manual effort.

## Contribution

It introduces a novel approach employing thousands of nuanced topics for automatic cross-reference generation, improving efficiency over manual annotation.

## Key findings

- System effectively identifies topically related verse pairs.
- Cost reduction compared to manual cross-reference creation.
- Demonstrates feasibility of automated, fine-grained topic-based cross-referencing.

## Abstract

Cross-referencing, which links passages of text to other related passages, can be a valuable study aid for facilitating comprehension of a text. However, cross-referencing requires first, a comprehensive thematic knowledge of the entire corpus, and second, a focused search through the corpus specifically to find such useful connections. Due to this, cross-reference resources are prohibitively expensive and exist only for the most well-studied texts (e.g. religious texts). We develop a topic-based system for automatically producing candidate cross-references which can be easily verified by human annotators. Our system utilizes fine-grained topic modeling with thousands of highly nuanced and specific topics to identify verse pairs which are topically related. We demonstrate that our system can be cost effective compared to having annotators acquire the expertise necessary to produce cross-reference resources unaided.