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清华大学学报(自然科学版)  2020, Vol. 60 Issue (5): 430-439    DOI: 10.16511/j.cnki.qhdxxb.2020.21.003
  专题:计算语言学 本期目录 | 过刊浏览 | 高级检索 |
基于深度学习的多语言跨领域主题对齐模型
余传明1, 原赛2, 胡莎莎1, 安璐3
1. 中南财经政法大学 信息与安全工程学院, 武汉 430073;
2. 中南财经政法大学 统计与数学学院, 武汉 430073;
3. 武汉大学 信息管理学院, 武汉 430072
Deep learning multi-language topic alignment model across domains
YU Chuanming1, YUAN Sai2, HU Shasha1, AN Lu3
1. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China;
2. School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, China;
3. School of Information Management, Wuhan University, Wuhan 430072, China
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摘要 在主题深度表示学习的基础上,该文提出了一种融合双语词嵌入的主题对齐模型(topic alignment model,TAM),通过双语词嵌入扩充语义对齐词汇词典,在传统双语主题模型基础上设计辅助分布用于改进不同词分布的语义共享,以此改善跨语言和跨领域情境下的主题对齐效果;提出了2种新的指标,即双语主题相似度(bilingual topic similarity,BTS)和双语对齐相似度(bilingual alignment similarity,BAS),用于评价辅助分布对齐的效果。相比传统的对齐模型MCTA,TAM在跨语言主题对齐任务中双语对齐相似度提升了约1.5%,在跨领域主题对齐任务中F1值提升了约10%。研究结果对于改进跨语言和跨领域信息处理具有重要意义。
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余传明
原赛
胡莎莎
安璐
关键词 跨语言主题对齐跨领域主题对齐深度学习双语词嵌入知识对齐    
Abstract: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.
Key wordscross-lingual topic alignment    cross-domain topic alignment    deep learning    bilingual word embedding    knowledge alignment
收稿日期: 2019-06-15      出版日期: 2020-04-26
基金资助:安璐,教授,E-mail:anlu97@163.com
引用本文:   
余传明, 原赛, 胡莎莎, 安璐. 基于深度学习的多语言跨领域主题对齐模型[J]. 清华大学学报(自然科学版), 2020, 60(5): 430-439.
YU Chuanming, YUAN Sai, HU Shasha, AN Lu. Deep learning multi-language topic alignment model across domains. Journal of Tsinghua University(Science and Technology), 2020, 60(5): 430-439.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2020.21.003  或          http://jst.tsinghuajournals.com/CN/Y2020/V60/I5/430
  
  
  
  
  
  
  
  
  
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