Mining multilingual topics from wikipedia

Ni, Xiaochuan; Sun, Ju; Hu, Jian; Chen, Zheng

doi:10.1145/1526709.1526904

Cited by 79 publications

(55 citation statements)

References 3 publications

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“…Wikipedia's multilinguality is used in the context of natural language processing, e.g. for topic mining in [11]. A possible use of multilingual data is Question Answering over Linked Data, as in [1,7].…”

Section: Background and Related Workmentioning

confidence: 99%

A Glimpse into Babel

Kaffee

Piscopo

Vougiouklis

et al. 2017

Proceedings of the 13th International Symposium on Open Collaboration

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Multilinguality is an important topic for knowledge bases, especially Wikidata, that was build to serve the multilingual requirements of an international community. Its labels are the way for humans to interact with the data. In this paper, we explore the state of languages in Wikidata as of now, especially in regard to its ontology, and the relationship to Wikipedia. Furthermore, we set the multilinguality of Wikidata in the context of the real world by comparing it to the distribution of native speakers. We find an existing language maldistribution, which is less urgent in the ontology, and promising results for future improvements.

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Section: Background and Related Workmentioning

confidence: 99%

A Glimpse into Babel

Kaffee

Piscopo

Vougiouklis

et al. 2017

Proceedings of the 13th International Symposium on Open Collaboration

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“…[26] use bilingual topic models trained on parallel corpora for word alignment and machine translation, where the bilingual topic models better capture the context in which a word is translated. Recently, a approach similar to ours was presented in [19].…”

Section: Related Researchmentioning

confidence: 99%

Cross-language linking of news stories on the web using interlingual topic modelling

Smet

Moens

2009

Proceedings of the 2nd ACM Workshop on Social Web Search and Mining

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We have studied the problem of linking event information across different languages without the use of translation systems or dictionaries. The linking is based on interlingua information obtained through probabilistic topic models trained on comparable corpora written in two languages (in our case English and Dutch). The achieve this, we expand the Latent Dirichlet Allocation model to process documents in two languages. We demonstrate the validity of the learned interlingual topics in a document clustering task, where the evaluation is performed on Google News.

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“…In CorrLDA [10] authors propose an asymmetric model to match words and pictures, even in this model both the image and its corresponding words are generated simultaneously. Recently [11] propose an extension of LDA to mine multilingual topics from Wikipedia articles by forcing aligned articles to share at least one topical distribution. All these approaches critically require alignments at the document level to mine the multilingual topic models and hence can't be applied to a comparable corpora.…”

Section: Topic 3 (E) Topic 16 (S) Topic 6 (S) Topic 18 (E) Topic 10 (S)mentioning

confidence: 99%

Extracting Multilingual Topics from Unaligned Comparable Corpora

Jagarlamudi

Daumé

2010

Lecture Notes in Computer Science

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Abstract. Topic models have been studied extensively in the context of monolingual corpora. Though there are some attempts to mine topical structure from cross-lingual corpora, they require clues about document alignments. In this paper we present a generative model called JointLDA which uses a bilingual dictionary to mine multilingual topics from an unaligned corpus. Experiments conducted on different data sets confirm our conjecture that jointly modeling the cross-lingual corpora offers several advantages compared to individual monolingual models. Since the JointLDA model merges related topics in different languages into a single multilingual topic: a) it can fit the data with relatively fewer topics. b) it has the ability to predict related words from a language different than that of the given document. In fact it has better predictive power compared to the bag-of-word based translation model leaving the possibility for JointLDA to be preferred over bag-of-word model for cross-lingual IR applications. We also found that the monolingual models learnt while optimizing the cross-lingual copora are more effective than the corresponding LDA models.

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Mining multilingual topics from wikipedia

Cited by 79 publications

References 3 publications

A Glimpse into Babel

A Glimpse into Babel

Cross-language linking of news stories on the web using interlingual topic modelling

Extracting Multilingual Topics from Unaligned Comparable Corpora

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