A word embedding-based approach to cross-lingual topic modeling
Abstract The cross-lingual topic analysis aims at extracting latent topics from corpora of different languages. Early approaches rely on high-cost multilingual resources (e.g., a parallel corpus), which is hard to come by in many real cases. Some works only require a translation dictionary as a link...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chang, Chia-Hsuan [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Knowledge and information systems - Springer London, 2000, 63(2021), 6 vom: 24. Apr., Seite 1529-1555 |
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| Übergeordnetes Werk: |
volume:63 ; year:2021 ; number:6 ; day:24 ; month:04 ; pages:1529-1555 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10115-021-01555-7 |
|---|
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OLC2125661241 |
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| 520 | |a Abstract The cross-lingual topic analysis aims at extracting latent topics from corpora of different languages. Early approaches rely on high-cost multilingual resources (e.g., a parallel corpus), which is hard to come by in many real cases. Some works only require a translation dictionary as a linkage between languages; however, when given an inappropriate dictionary (e.g., small coverage of dictionary), the cross-lingual topic model would shrink to a monolingual topic model and generate less diversified topics. Therefore, it is imperative to investigate a cross-lingual topic model requiring fewer bilingual resources. Recently, some space-mapping techniques have been proposed to help align multiple word embedding of different languages into a quality cross-lingual word embedding by referring to a small number of translation pairs. This work proposes a cross-lingual topic model, called Cb-CLTM, which incorporates with cross-lingual word embedding. To leverage the power of word semantics and the linkage between languages from the cross-lingual word embedding, the Cb-CLTM considers each word as a continuous embedding vector rather than a discrete word type. The experiments demonstrate that, when cross-lingual word space exhibits strong isomorphism, Cb-CLTM can generate more coherent topics with higher diversity and induce better representations of documents across languages for further tasks such as cross-lingual document clustering and classification. When the cross-lingual word space is less isomorphic, Cb-CLTM generates less coherent topics yet still prevails in topic diversity and document classification. | ||
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10.1007/s10115-021-01555-7 doi (DE-627)OLC2125661241 (DE-He213)s10115-021-01555-7-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Chang, Chia-Hsuan verfasserin (orcid)0000-0001-9116-8244 aut A word embedding-based approach to cross-lingual topic modeling 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract The cross-lingual topic analysis aims at extracting latent topics from corpora of different languages. Early approaches rely on high-cost multilingual resources (e.g., a parallel corpus), which is hard to come by in many real cases. Some works only require a translation dictionary as a linkage between languages; however, when given an inappropriate dictionary (e.g., small coverage of dictionary), the cross-lingual topic model would shrink to a monolingual topic model and generate less diversified topics. Therefore, it is imperative to investigate a cross-lingual topic model requiring fewer bilingual resources. Recently, some space-mapping techniques have been proposed to help align multiple word embedding of different languages into a quality cross-lingual word embedding by referring to a small number of translation pairs. This work proposes a cross-lingual topic model, called Cb-CLTM, which incorporates with cross-lingual word embedding. To leverage the power of word semantics and the linkage between languages from the cross-lingual word embedding, the Cb-CLTM considers each word as a continuous embedding vector rather than a discrete word type. The experiments demonstrate that, when cross-lingual word space exhibits strong isomorphism, Cb-CLTM can generate more coherent topics with higher diversity and induce better representations of documents across languages for further tasks such as cross-lingual document clustering and classification. When the cross-lingual word space is less isomorphic, Cb-CLTM generates less coherent topics yet still prevails in topic diversity and document classification. Cross-language Cross-lingual topic model Cross-lingual word embedding Hwang, San-Yih aut Enthalten in Knowledge and information systems Springer London, 2000 63(2021), 6 vom: 24. Apr., Seite 1529-1555 (DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 0219-1377 nnns volume:63 year:2021 number:6 day:24 month:04 pages:1529-1555 https://doi.org/10.1007/s10115-021-01555-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 54.64$jDatenbanken VZ 106410865 (DE-625)106410865 AR 63 2021 6 24 04 1529-1555 |
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004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl A word embedding-based approach to cross-lingual topic modeling Cross-language Cross-lingual topic model Cross-lingual word embedding |
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Chang, Chia-Hsuan |
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a word embedding-based approach to cross-lingual topic modeling |
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A word embedding-based approach to cross-lingual topic modeling |
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Abstract The cross-lingual topic analysis aims at extracting latent topics from corpora of different languages. Early approaches rely on high-cost multilingual resources (e.g., a parallel corpus), which is hard to come by in many real cases. Some works only require a translation dictionary as a linkage between languages; however, when given an inappropriate dictionary (e.g., small coverage of dictionary), the cross-lingual topic model would shrink to a monolingual topic model and generate less diversified topics. Therefore, it is imperative to investigate a cross-lingual topic model requiring fewer bilingual resources. Recently, some space-mapping techniques have been proposed to help align multiple word embedding of different languages into a quality cross-lingual word embedding by referring to a small number of translation pairs. This work proposes a cross-lingual topic model, called Cb-CLTM, which incorporates with cross-lingual word embedding. To leverage the power of word semantics and the linkage between languages from the cross-lingual word embedding, the Cb-CLTM considers each word as a continuous embedding vector rather than a discrete word type. The experiments demonstrate that, when cross-lingual word space exhibits strong isomorphism, Cb-CLTM can generate more coherent topics with higher diversity and induce better representations of documents across languages for further tasks such as cross-lingual document clustering and classification. When the cross-lingual word space is less isomorphic, Cb-CLTM generates less coherent topics yet still prevails in topic diversity and document classification. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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Abstract The cross-lingual topic analysis aims at extracting latent topics from corpora of different languages. Early approaches rely on high-cost multilingual resources (e.g., a parallel corpus), which is hard to come by in many real cases. Some works only require a translation dictionary as a linkage between languages; however, when given an inappropriate dictionary (e.g., small coverage of dictionary), the cross-lingual topic model would shrink to a monolingual topic model and generate less diversified topics. Therefore, it is imperative to investigate a cross-lingual topic model requiring fewer bilingual resources. Recently, some space-mapping techniques have been proposed to help align multiple word embedding of different languages into a quality cross-lingual word embedding by referring to a small number of translation pairs. This work proposes a cross-lingual topic model, called Cb-CLTM, which incorporates with cross-lingual word embedding. To leverage the power of word semantics and the linkage between languages from the cross-lingual word embedding, the Cb-CLTM considers each word as a continuous embedding vector rather than a discrete word type. The experiments demonstrate that, when cross-lingual word space exhibits strong isomorphism, Cb-CLTM can generate more coherent topics with higher diversity and induce better representations of documents across languages for further tasks such as cross-lingual document clustering and classification. When the cross-lingual word space is less isomorphic, Cb-CLTM generates less coherent topics yet still prevails in topic diversity and document classification. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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Abstract The cross-lingual topic analysis aims at extracting latent topics from corpora of different languages. Early approaches rely on high-cost multilingual resources (e.g., a parallel corpus), which is hard to come by in many real cases. Some works only require a translation dictionary as a linkage between languages; however, when given an inappropriate dictionary (e.g., small coverage of dictionary), the cross-lingual topic model would shrink to a monolingual topic model and generate less diversified topics. Therefore, it is imperative to investigate a cross-lingual topic model requiring fewer bilingual resources. Recently, some space-mapping techniques have been proposed to help align multiple word embedding of different languages into a quality cross-lingual word embedding by referring to a small number of translation pairs. This work proposes a cross-lingual topic model, called Cb-CLTM, which incorporates with cross-lingual word embedding. To leverage the power of word semantics and the linkage between languages from the cross-lingual word embedding, the Cb-CLTM considers each word as a continuous embedding vector rather than a discrete word type. The experiments demonstrate that, when cross-lingual word space exhibits strong isomorphism, Cb-CLTM can generate more coherent topics with higher diversity and induce better representations of documents across languages for further tasks such as cross-lingual document clustering and classification. When the cross-lingual word space is less isomorphic, Cb-CLTM generates less coherent topics yet still prevails in topic diversity and document classification. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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