C-BiLDA extracting cross-lingual topics from non-parallel texts by distinguishing shared from unshared content.
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| Title: | C-BiLDA extracting cross-lingual topics from non-parallel texts by distinguishing shared from unshared content. |
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| 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] |
| Copyright of Data Mining & Knowledge Discovery is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: C-BiLDA extracting cross-lingual topics from non-parallel texts by distinguishing shared from unshared content. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Heyman%2C+Geert%22">Heyman, Geert</searchLink><relatesTo>1</relatesTo><i> geert.heyman@cs.kuleuven.be</i><br /><searchLink fieldCode="AR" term="%22Vulić%2C+Ivan%22">Vulić, Ivan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Moens%2C+Marie-Francine%22">Moens, Marie-Francine</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Data+Mining+%26+Knowledge+Discovery%22">Data Mining & Knowledge Discovery</searchLink>. Sep2016, Vol. 30 Issue 5, p1299-1323. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multilingual+computing%22">Multilingual computing</searchLink><br /><searchLink fieldCode="DE" term="%22Text+mining%22">Text mining</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmark+problems+%28Computer+science%29%22">Benchmark problems (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+transfer%22">Knowledge transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Approximation+algorithms%22">Approximation algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Data Mining & Knowledge Discovery is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10618-015-0442-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 1299 Subjects: – SubjectFull: Multilingual computing Type: general – SubjectFull: Text mining Type: general – SubjectFull: Benchmark problems (Computer science) Type: general – SubjectFull: Knowledge transfer Type: general – SubjectFull: Approximation algorithms Type: general Titles: – TitleFull: C-BiLDA extracting cross-lingual topics from non-parallel texts by distinguishing shared from unshared content. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Heyman, Geert – PersonEntity: Name: NameFull: Vulić, Ivan – PersonEntity: Name: NameFull: Moens, Marie-Francine IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 13845810 Numbering: – Type: volume Value: 30 – Type: issue Value: 5 Titles: – TitleFull: Data Mining & Knowledge Discovery Type: main |
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