Abstract
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) |
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Title of host publication | Proceedings of the Eight nternational Joint Conference on Natural Language Processing (IJCNLP 2017) |
Pages | 947-956 |
Number of pages | 10 |
Volume | 1 |
State | Published - 2017 |