Generation of conceptual-level text cloud with graph diffusion

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

Generation of conceptual-level text cloud with graph diffusion

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

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016
Editors	Chung-Hsien Wu, Yuen-Hsien Tseng, Hung-Yu Kao, Lun-Wei Ku, Yu Tsao, Shih-Hung Wu
Publisher	The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Pages	402-411
Number of pages	10
ISBN (Electronic)	9789573079293
Publication status	Published - 2016 Oct 1
Event	28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016 - Tainan, Taiwan Duration: 2016 Oct 6 → 2016 Oct 7

Publication series

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

Conference

Conference	28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016
Country	Taiwan
City	Tainan
Period	16-10-06 → 16-10-07

All Science Journal Classification (ASJC) codes

Speech and Hearing
Language and Linguistics

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Lin, Y. C., Yang, P. A., Lee, Y. K., & Chuang, K. T. (2016). Generation of conceptual-level text cloud with graph diffusion. In C-H. Wu, Y-H. Tseng, H-Y. Kao, L-W. Ku, Y. Tsao, & S-H. Wu (Eds.), Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016 (pp. 402-411). (Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016). The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).

Lin, Ying Chun ; Yang, Po An ; Lee, Yen Kuan ; Chuang, Kun Ta. / Generation of conceptual-level text cloud with graph diffusion. Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016. editor / Chung-Hsien Wu ; Yuen-Hsien Tseng ; Hung-Yu Kao ; Lun-Wei Ku ; Yu Tsao ; Shih-Hung Wu. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 2016. pp. 402-411 (Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016).

@inproceedings{93772f99e2004b11a73f5a542c5fb245,

title = "Generation of conceptual-level text cloud with graph diffusion",

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.",

author = "Lin, {Ying Chun} and Yang, {Po An} and Lee, {Yen Kuan} and Chuang, {Kun Ta}",

year = "2016",

month = oct,

day = "1",

language = "English",

series = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016",

publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",

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editor = "Chung-Hsien Wu and Yuen-Hsien Tseng and Hung-Yu Kao and Lun-Wei Ku and Yu Tsao and Shih-Hung Wu",

booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016",

note = "28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016 ; Conference date: 06-10-2016 Through 07-10-2016",

}

Lin, YC, Yang, PA, Lee, YK & Chuang, KT 2016, Generation of conceptual-level text cloud with graph diffusion. in C-H Wu, Y-H Tseng, H-Y Kao, L-W Ku, Y Tsao & S-H Wu (eds), Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016. Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016, The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), pp. 402-411, 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016, Tainan, Taiwan, 16-10-06.

Generation of conceptual-level text cloud with graph diffusion. / Lin, Ying Chun; Yang, Po An; Lee, Yen Kuan; Chuang, Kun Ta.

Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016. ed. / Chung-Hsien Wu; Yuen-Hsien Tseng; Hung-Yu Kao; Lun-Wei Ku; Yu Tsao; Shih-Hung Wu. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 2016. p. 402-411 (Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Generation of conceptual-level text cloud with graph diffusion

AU - Lin, Ying Chun

AU - Yang, Po An

AU - Lee, Yen Kuan

AU - Chuang, Kun Ta

PY - 2016/10/1

Y1 - 2016/10/1

N2 - 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.

AB - 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.

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BT - Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016

A2 - Wu, Chung-Hsien

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PB - The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)

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Lin YC, Yang PA, Lee YK, Chuang KT. Generation of conceptual-level text cloud with graph diffusion. In Wu C-H, Tseng Y-H, Kao H-Y, Ku L-W, Tsao Y, Wu S-H, editors, Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP). 2016. p. 402-411. (Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, ROCLING 2016).