Papers › Semantic Document Distance Measures and Unsupervised Document Revision Detection
Semantic Document Distance Measures and Unsupervised Document Revision Detection
Xiaofeng Zhu, Diego Klabjan, Patrick Bless
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.
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