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        <title>Semantic Document Distance Measures and Unsupervised Document Revision Detection</title>
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    <name type="personal">
        <namePart type="given">Xiaofeng</namePart>
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        <namePart type="given">Diego</namePart>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)</title>
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        <name type="personal">
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    <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.</abstract>
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%0 Conference Proceedings
%T Semantic Document Distance Measures and Unsupervised Document Revision Detection
%A Zhu, Xiaofeng
%A Klabjan, Diego
%A Bless, Patrick
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F zhu-etal-2017-semantic
%X 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.
%U https://aclanthology.org/I17-1095
%P 947-956

Markdown (Informal)

[Semantic Document Distance Measures and Unsupervised Document Revision Detection](https://aclanthology.org/I17-1095) (Zhu et al., IJCNLP 2017)

• Semantic Document Distance Measures and Unsupervised Document Revision Detection (Zhu et al., IJCNLP 2017)

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.