A Transformation Approach between the Similar Language Text

Dawa Yidemucao; Aniwar  Tohuti; Qing Yu; Maimaiti Roukeyanmu

doi:10.4028/www.scientific.net/AMR.791-793.1716

Paper Titles

Research of Network Testing Based on MPLS L2VPN
p.1695

Moving Icons on Windows XP Desktop Efficiently, Exactly and Eliminating Icons Bounce after Releasing
p.1699

Research on Structure Model of Building Information Modeling Technology
p.1704

Research on the Actual Combat-Oriented Mode of Network Security Supervision Based on Networked Resources
p.1710

A Transformation Approach between the Similar Language Text
p.1716

Integrating Knowledge Requirements into Topic Map for Online-Education
p.1721

A Worst-Case Execution Time Analysis Approach Based on AOE Networks
p.1726

An Improvement for Process Management in Android Vehicle Terminal
p.1730

The Application of Multimedia-Based Technology
p.1735

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 791-793A Transformation Approach between the Similar...

A Transformation Approach between the Similar Language Text

Abstract:

This paper first investigates the similarity level between the same family and closer languages (such as Altai family languages) and then examines a transformation between their entries and texts. Cosine similarity measure and dynamic programming (DP) algorithm are used to calculate the similarity and transformation between the source and target languages using a multilingual parallel data set and a function word dictionary. Test data set includes 7,854 paralleled sentences of Chinese, Uyghur, Kazakh and Mongolian various writing systems. Experimental results show that the similarity level of the languages from the same language branch is higher than that between different language branches. And a transformation test focused on the Mongolian language branch showed accuracy of 86.7% for NM to TM and 91.1% for NM to TODO.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 791-793)

Pages:

1716-1720

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.791-793.1716

Citation:

Cite this paper

Online since:

September 2013

Authors:

Dawa Yidemucao, Aniwar Tohuti, Qing Yu, Maimaiti Roukeyanmu

Keywords:

DP Algorithm, Minority Language, Similar Language, Similarity, Text Transformation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Shi-Kuo Chang, Jong-Hyeok Lee and Kam-Fai Wong, COMPUTER PROCESSING OF ORIENTAL LANGUAGES, Vol. 19(2&3), World Scientific (2006).

Google Scholar

[2] T. Schultz and A. Waibel, "Fast Bootstrapping of LVCSR System with Multilingual phoneme Sets, Proc. Eurospeech, 1, pp.371-374 (2001).

DOI: 10.21437/eurospeech.1997-141

Google Scholar

[3] Lin jun Zhang, et. al, Cross-Language information retrival, Journal of Computer Science, Vol. 31(7), pp.16-19 (2004).

Google Scholar

[4] Shen gwei Tian, et. al, A method fro Uyghur Sentence Similarity Computation, Journal of Computer Engineering and Application, China, Vol49(26), pp.144-146 (2009).

Google Scholar

[5] Idomucogiin Dawa and Satoshi Nakamura, A Study on Cross Transformation of Mongolian Language, Journal Natural Language Processing, Vol. 15(5), pp.3-21 ( 2008, ).

DOI: 10.5715/jnlp.15.5_3

Google Scholar

[6] Jun. Ye, Cosine Similarity measures for intuitionistic fuzzy sets and their Applications,. Mathmatical and Computer Modeling, Vol. 53, pp.91-97 (2011).

DOI: 10.1016/j.mcm.2010.07.022

Google Scholar

[7] Landau G. M, et al, Two algorithms for LCS consecutive suffix alignment, In Proc. 15th Ann. Simp. On combinatorial Pattern Matching, LNCS 3109, pp.173-193 (2004).

DOI: 10.1007/978-3-540-27801-6_13

Google Scholar

[8] Francois Nicolas, Eric Rivals, Longest common subsequence problem for unoriented and cyclic strings, J. Theoretical Computer Science 370, pp.1-18 (2007).

DOI: 10.1016/j.tcs.2006.10.002

Google Scholar