Digesting Multilingual Reader Comments via Latent Discussion Topics with Commonality and Specificity
Refereed conference paper presented and published in conference proceedings


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摘要Many news websites from different regions in the world allow readers to write comments in their own languages about an event. Digesting such enormous amount of comments in different languages is difficult. One elegant way to digest and organize these comments is to detect latent discussion topics with the consideration of language
attributes. Some discussion topics are common topics shared between languages whereas some topics are specifically dominated by a particular language. To tackle this task of discovering discussion
topics that exhibit commonality or specificity from news reader comments written in different languages, we propose a new model called TDCS based on graphical models, which can cope with the language gap and detect language-common and language specific latent discussion topics simultaneously. Our TDCS model also exploits comment-oriented clues via a scalable Dirichlet Multinomial Regression method. To learn the model parameters, we develop
an inference method which alternates between EM and Gibbs sampling. Experimental results show that our proposed TDCS model can provide an effective way to digest multilingual news reader comments.
著者Bei SHI, Wai Lam, Lidong Bing, Yinqing Xu
會議名稱The 25th ACM International Conference on Information and Knowledge Management (CIKM 2016)
會議開始日24.10.2016
會議完結日28.10.2016
會議地點Indianapolis
會議國家/地區美國
會議論文集題名CIKM '16 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
出版年份2016
月份10
日期24
出版社ACM
頁次2293 - 2298
國際標準書號978-1-4503-4073-1
語言美式英語

上次更新時間 2021-19-01 於 02:04