Difference between revisions of "Cross-Lingual Blog Analysis based on Multilingual Blog Distillation from Multilingual Wikipedia Entries"
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| − | '''Cross-Lingual Blog Analysis based on Multilingual Blog Distillation from Multilingual Wikipedia Entries''' - scientific work related to Wikipedia quality published in 2008, written by Mariko Kawaba, Hiroyuki Nakasaki, Takehito Utsuro and Tomohiro Fukuhara. | + | '''Cross-Lingual Blog Analysis based on Multilingual Blog Distillation from Multilingual Wikipedia Entries''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Mariko Kawaba]], [[Hiroyuki Nakasaki]], [[Takehito Utsuro]] and [[Tomohiro Fukuhara]]. |
== Overview == | == Overview == | ||
| − | The goal of this paper is to cross- | + | The goal of this paper is to [[cross-lingual]]ly analyze [[multilingual]] blogs collected with a topic keyword. The framework of collecting multilingual blogs with a topic keyword is designed as the blog distillation (feed search) procedure. Mulitlingual queries for retrieving blog feeds are created from [[Wikipedia]] entries. Finally, authors cross-lingually and crossculturally compare less well known facts and opinions that are closely related to a given topic. Preliminary evaluation results support the effectiveness of the proposed framework. |
Revision as of 09:26, 20 October 2019
Cross-Lingual Blog Analysis based on Multilingual Blog Distillation from Multilingual Wikipedia Entries - scientific work related to Wikipedia quality published in 2008, written by Mariko Kawaba, Hiroyuki Nakasaki, Takehito Utsuro and Tomohiro Fukuhara.
Overview
The goal of this paper is to cross-lingually analyze multilingual blogs collected with a topic keyword. The framework of collecting multilingual blogs with a topic keyword is designed as the blog distillation (feed search) procedure. Mulitlingual queries for retrieving blog feeds are created from Wikipedia entries. Finally, authors cross-lingually and crossculturally compare less well known facts and opinions that are closely related to a given topic. Preliminary evaluation results support the effectiveness of the proposed framework.
