Difference between revisions of "Mining Multilingual Topics from Wikipedia"
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| − | '''Mining Multilingual Topics from Wikipedia''' - scientific work related to Wikipedia quality published in 2009, written by Xiaochuan Ni, Jian-Tao Sun, Jian Hu and Zheng Chen. | + | '''Mining Multilingual Topics from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Xiaochuan Ni]], [[Jian-Tao Sun]], [[Jian Hu]] and [[Zheng Chen]]. |
== Overview == | == Overview == | ||
| − | In this paper, authors try to leverage a large-scale and multilingual knowledge base, Wikipedia, to help effectively analyze and organize Web information written in different | + | In this paper, authors try to leverage a large-scale and [[multilingual]] knowledge base, [[Wikipedia]], to help effectively analyze and organize Web information written in [[different language]]s. Based on the observation that one Wikipedia concept may be described by articles in different languages, authors adapt existing topic modeling algorithm for mining multilingual topics from this knowledge base. The extracted 'universal' topics have multiple types of representations, with each type corresponding to one language. Accordingly, new documents of different languages can be represented in a space using a group of universal topics, which makes various multilingual Web applications feasible. |
Revision as of 06:49, 28 August 2019
Mining Multilingual Topics from Wikipedia - scientific work related to Wikipedia quality published in 2009, written by Xiaochuan Ni, Jian-Tao Sun, Jian Hu and Zheng Chen.
Overview
In this paper, authors try to leverage a large-scale and multilingual knowledge base, Wikipedia, to help effectively analyze and organize Web information written in different languages. Based on the observation that one Wikipedia concept may be described by articles in different languages, authors adapt existing topic modeling algorithm for mining multilingual topics from this knowledge base. The extracted 'universal' topics have multiple types of representations, with each type corresponding to one language. Accordingly, new documents of different languages can be represented in a space using a group of universal topics, which makes various multilingual Web applications feasible.
