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 1 and simulated data sets.
|Original language||English (US)|
|Title of host publication||Proceedings of the Eight nternational Joint Conference on Natural Language Processing (IJCNLP 2017)|
|Number of pages||10|
|State||Published - 2017|