C-BiLDA extracting cross-lingual topics from non-parallel texts by distinguishing shared from unshared content.

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Bibliographic Details
Title:	C-BiLDA extracting cross-lingual topics from non-parallel texts by distinguishing shared from unshared content.
Authors:	Heyman, Geert¹ geert.heyman@cs.kuleuven.be, Vulić, Ivan¹, Moens, Marie-Francine¹
Source:	Data Mining & Knowledge Discovery. Sep2016, Vol. 30 Issue 5, p1299-1323. 25p.
Subjects:	Multilingual computing, Text mining, Benchmark problems (Computer science), Knowledge transfer, Approximation algorithms
Abstract:	We study the problem of extracting cross-lingual topics from non-parallel multilingual text datasets with partially overlapping thematic content (e.g., aligned Wikipedia articles in two different languages). To this end, we develop a new bilingual probabilistic topic model called comparable bilingual latent Dirichlet allocation (C-BiLDA), which is able to deal with such comparable data, and, unlike the standard bilingual LDA model (BiLDA), does not assume the availability of document pairs with identical topic distributions. We present a full overview of C-BiLDA, and show its utility in the task of cross-lingual knowledge transfer for multi-class document classification on two benchmarking datasets for three language pairs. The proposed model outperforms the baseline LDA model, as well as the standard BiLDA model and two standard low-rank approximation methods (CL-LSI and CL-KCCA) used in previous work on this task. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	We study the problem of extracting cross-lingual topics from non-parallel multilingual text datasets with partially overlapping thematic content (e.g., aligned Wikipedia articles in two different languages). To this end, we develop a new bilingual probabilistic topic model called comparable bilingual latent Dirichlet allocation (C-BiLDA), which is able to deal with such comparable data, and, unlike the standard bilingual LDA model (BiLDA), does not assume the availability of document pairs with identical topic distributions. We present a full overview of C-BiLDA, and show its utility in the task of cross-lingual knowledge transfer for multi-class document classification on two benchmarking datasets for three language pairs. The proposed model outperforms the baseline LDA model, as well as the standard BiLDA model and two standard low-rank approximation methods (CL-LSI and CL-KCCA) used in previous work on this task. [ABSTRACT FROM AUTHOR]
ISSN:	13845810
DOI:	10.1007/s10618-015-0442-x