Deep learning multi-language topic alignment model across domains

被引：0
|
作者：
Yu C. [1 ]
Yuan S. [2 ]
Hu S. [1 ]
An L. [3 ]
机构：
[1] School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan
[2] School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan
[3] School of Information Management, Wuhan University, Wuhan
来源：
An, Lu (anlu97@163.com) | 1600年 / Tsinghua University卷 / 60期
关键词：
Bilingual word embedding; Cross-domain topic alignment; Cross-lingual topic alignment; Deep learning; Knowledge alignment;
D O I：
10.16511/j.cnki.qhdxxb.2020.21.003
中图分类号：
学科分类号：
摘要：
Deep representation learning of domain topics was used to build a topic alignment model (TAM) with integrated bilingual word embedding. The semantic alignment lexicon was extended to include bilingual word embedding. A traditional bilingual topic model was used to develop an auxiliary distribution to improve the word distribution semantic sharing to improve the topic alignments in the cross-lingual and cross-domain contexts. A bilingual topic similarity (BTS) indicator and a bilingual alignment similarity (BAS) indicator were developed to evaluate the supplementary alignment. The bilingual alignment similarity improved the cross-language topic matching by about 1.5% compared to a traditional multi-language common cultural theme analysis and improved F1 by about 10% for cross-domain topic alignment. These results can improve cross language and cross domain information processing. © 2020, Tsinghua University Press. All right reserved.
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页码：430 / 439
页数：9
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