Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition

GE Tao; DOU Qing; JI Heng; CUI Lei; CHANG Baobao; PAN Xiaoman; SUI Zhifang; ZHOU Ming

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Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition

GE Tao 1, DOU Qing 2, JI Heng 3, CUI Lei 4, CHANG Baobao 1, PAN Xiaoman 3, SUI Zhifang 1, ZHOU Ming 4

1. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871

2. Facebook, Menlo Park, CA, USA, 94025

3. Department of Computer Science, Rensselaer Polytechnic Institute, USA, 12180

4. Microsoft Research, Beijing, 100080

*Correspondence author

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Funding: the Research Fund for the Doctoral Program of Higher Education （No.20130001110027）

Opened online:28 April 2017

Accepted by: none

Citation: GE Tao, DOU Qing, JI Heng.Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition[OL]. [28 April 2017] http://en.paper.edu.cn/en_releasepaper/content/4726125

This paper proposes to study fine-grained coordinated cross-lingual text stream alignment through a novel information network decipherment paradigm. We use Burst Information Networks as media to represent the text streams and present a simple yet effective network decipherment algorithm with diverse clues to decipher the networks for accurate text stream alignment. Extensive experiments on Chinese-English coordinated news streams show our approach can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining.

Keywords:Burst Information Networks, Text stream alignment, Network decipherment, language knowledge mining

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