Semantic Document Distance Measures and Unsupervised Document Revision Detection

Xiaofeng Zhu, Diego Klabjan, Patrick Bless


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 and simulated data sets.
Anthology ID:
I17-1095
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Editors:
Greg Kondrak, Taro Watanabe
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
947–956
Language:
URL:
https://aclanthology.org/I17-1095
DOI:
Bibkey:
Cite (ACL):
Xiaofeng Zhu, Diego Klabjan, and Patrick Bless. 2017. Semantic Document Distance Measures and Unsupervised Document Revision Detection. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 947–956, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
Semantic Document Distance Measures and Unsupervised Document Revision Detection (Zhu et al., IJCNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/I17-1095.pdf
Software:
 I17-1095.Software.txt
Dataset:
 I17-1095.Datasets.txt
Code
 XiaofengZhu/wDTW-wTED