Generation of conceptual-level text cloud with graph diffusion

Ying Chun Lin, Po An Yang, Yen Kuan Lee, Kun Ta Chuang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we explore a novel framework to generate a well-known text cloud visualization with the conceptual sense. The traditional text cloud is usually generated according to the word occurrence, possibly including the idf-based concept for word weight. The solution is applicable for the long articles. However, for a set of short sentences such as daily news titles, we cannot easily understand the weight of each keyword and its importance to users since the idf value and occurrence in short sentences are difficult to be both well discriminative. In this paper, we propose a graph-based diffusion model to generate conceptual level keyword cloud. We utilize the RDF-based Wikipedia word relation and apply in the Chinese news titles from different news sources. The result shows that our visualization can easily capture the importance concept revealed in a set of news titles.

Original languageEnglish
Title of host publicationProceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016
EditorsChung-Hsien Wu, Yuen-Hsien Tseng, Hung-Yu Kao, Lun-Wei Ku, Yu Tsao, Shih-Hung Wu
PublisherThe Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Pages402-411
Number of pages10
ISBN (Electronic)9789573079293
Publication statusPublished - 2016 Oct 1
Event28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016 - Tainan, Taiwan
Duration: 2016 Oct 62016 Oct 7

Publication series

NameProceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016

Conference

Conference28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016
CountryTaiwan
CityTainan
Period16-10-0616-10-07

All Science Journal Classification (ASJC) codes

  • Speech and Hearing
  • Language and Linguistics

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