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.
@article{arxiv.1709.01256,
title = {Semantic Document Distance Measures and Unsupervised Document Revision Detection},
author = {Xiaofeng Zhu and Diego Klabjan and Patrick Bless},
journal= {arXiv preprint arXiv:1709.01256},
year = {2020}
}