# Semantic Document Distance Measures and Unsupervised Document Revision Detection

arXiv: https://arxiv.org/abs/1709.01256 · HTML: https://mlsanity.com/paper/1709.01256/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2017.

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

- [GVD: Governed Versioning and Deduplication for Document Repositories](https://mlsanity.com/paper/2609.17696/) (2026)

## Abstract (arXiv)

In this paper, we model the document revision detection problem as a minimum cost branching problem that relies on computing document distances. Furthermore, we propose two new document distance measures, word vector-based Dynamic Time Warping (wDTW) and word vector-based Tree Edit Distance (wTED). Our revision detection system is designed for a large scale corpus and implemented in Apache Spark. We demonstrate that our system can more precisely detect revisions than state-of-the-art methods by utilizing the Wikipedia revision dumps https://snap.stanford.edu/data/wiki-meta.html and simulated data sets.