Difference between revisions of "English-To-Traditional Chinese Cross-Lingual Link Discovery in Articles with Wikipedia Corpus"
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+ | {{Infobox work | ||
+ | | title = English-To-Traditional Chinese Cross-Lingual Link Discovery in Articles with Wikipedia Corpus | ||
+ | | date = 2012 | ||
+ | | authors = [[Liang-Pu Chen]]<br />[[Yu-Lun Shih]]<br />[[Chien-Ting Chen]]<br />[[Tsun Ku]]<br />[[Wen-Tai Hsieh]]<br />[[Hung-Sheng Chiu]]<br />[[Ren-Dar Yang]] | ||
+ | | link = http://www.aclweb.org/anthology/O12-1016 | ||
+ | }} | ||
'''English-To-Traditional Chinese Cross-Lingual Link Discovery in Articles with Wikipedia Corpus''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Liang-Pu Chen]], [[Yu-Lun Shih]], [[Chien-Ting Chen]], [[Tsun Ku]], [[Wen-Tai Hsieh]], [[Hung-Sheng Chiu]] and [[Ren-Dar Yang]]. | '''English-To-Traditional Chinese Cross-Lingual Link Discovery in Articles with Wikipedia Corpus''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Liang-Pu Chen]], [[Yu-Lun Shih]], [[Chien-Ting Chen]], [[Tsun Ku]], [[Wen-Tai Hsieh]], [[Hung-Sheng Chiu]] and [[Ren-Dar Yang]]. | ||
== Overview == | == Overview == | ||
In this paper, authors design a processing flow to produce linked data in articles, providing anchorbased term’s additional information and related terms in [[different language]]s (English to Chinese). [[Wikipedia]] has been a very important corpus and knowledge bank. Although Wikipedia describes itself not a dictionary or encyclopedia, it is if high potential values in applications and data mining researches. Link discovery is a useful IR application, based on Data Mining and NLP algorithms and has been used in several fields. According to the results of experiment, this method does make the result has improved. | In this paper, authors design a processing flow to produce linked data in articles, providing anchorbased term’s additional information and related terms in [[different language]]s (English to Chinese). [[Wikipedia]] has been a very important corpus and knowledge bank. Although Wikipedia describes itself not a dictionary or encyclopedia, it is if high potential values in applications and data mining researches. Link discovery is a useful IR application, based on Data Mining and NLP algorithms and has been used in several fields. According to the results of experiment, this method does make the result has improved. | ||
+ | |||
+ | == Embed == | ||
+ | === Wikipedia Quality === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Chen, Liang-Pu; Shih, Yu-Lun; Chen, Chien-Ting; Ku, Tsun; Hsieh, Wen-Tai; Chiu, Hung-Sheng; Yang, Ren-Dar. (2012). "[[English-To-Traditional Chinese Cross-Lingual Link Discovery in Articles with Wikipedia Corpus]]". | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === English Wikipedia === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | {{cite journal |last1=Chen |first1=Liang-Pu |last2=Shih |first2=Yu-Lun |last3=Chen |first3=Chien-Ting |last4=Ku |first4=Tsun |last5=Hsieh |first5=Wen-Tai |last6=Chiu |first6=Hung-Sheng |last7=Yang |first7=Ren-Dar |title=English-To-Traditional Chinese Cross-Lingual Link Discovery in Articles with Wikipedia Corpus |date=2012 |url=https://wikipediaquality.com/wiki/English-To-Traditional_Chinese_Cross-Lingual_Link_Discovery_in_Articles_with_Wikipedia_Corpus}} | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === HTML === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Chen, Liang-Pu; Shih, Yu-Lun; Chen, Chien-Ting; Ku, Tsun; Hsieh, Wen-Tai; Chiu, Hung-Sheng; Yang, Ren-Dar. (2012). &quot;<a href="https://wikipediaquality.com/wiki/English-To-Traditional_Chinese_Cross-Lingual_Link_Discovery_in_Articles_with_Wikipedia_Corpus">English-To-Traditional Chinese Cross-Lingual Link Discovery in Articles with Wikipedia Corpus</a>&quot;. | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | |||
+ | |||
+ | [[Category:Scientific works]] | ||
+ | [[Category:English Wikipedia]] | ||
+ | [[Category:Chinese Wikipedia]] |
Latest revision as of 12:27, 11 January 2021
Authors | Liang-Pu Chen Yu-Lun Shih Chien-Ting Chen Tsun Ku Wen-Tai Hsieh Hung-Sheng Chiu Ren-Dar Yang |
---|---|
Publication date | 2012 |
Links | Original |
English-To-Traditional Chinese Cross-Lingual Link Discovery in Articles with Wikipedia Corpus - scientific work related to Wikipedia quality published in 2012, written by Liang-Pu Chen, Yu-Lun Shih, Chien-Ting Chen, Tsun Ku, Wen-Tai Hsieh, Hung-Sheng Chiu and Ren-Dar Yang.
Overview
In this paper, authors design a processing flow to produce linked data in articles, providing anchorbased term’s additional information and related terms in different languages (English to Chinese). Wikipedia has been a very important corpus and knowledge bank. Although Wikipedia describes itself not a dictionary or encyclopedia, it is if high potential values in applications and data mining researches. Link discovery is a useful IR application, based on Data Mining and NLP algorithms and has been used in several fields. According to the results of experiment, this method does make the result has improved.
Embed
Wikipedia Quality
Chen, Liang-Pu; Shih, Yu-Lun; Chen, Chien-Ting; Ku, Tsun; Hsieh, Wen-Tai; Chiu, Hung-Sheng; Yang, Ren-Dar. (2012). "[[English-To-Traditional Chinese Cross-Lingual Link Discovery in Articles with Wikipedia Corpus]]".
English Wikipedia
{{cite journal |last1=Chen |first1=Liang-Pu |last2=Shih |first2=Yu-Lun |last3=Chen |first3=Chien-Ting |last4=Ku |first4=Tsun |last5=Hsieh |first5=Wen-Tai |last6=Chiu |first6=Hung-Sheng |last7=Yang |first7=Ren-Dar |title=English-To-Traditional Chinese Cross-Lingual Link Discovery in Articles with Wikipedia Corpus |date=2012 |url=https://wikipediaquality.com/wiki/English-To-Traditional_Chinese_Cross-Lingual_Link_Discovery_in_Articles_with_Wikipedia_Corpus}}
HTML
Chen, Liang-Pu; Shih, Yu-Lun; Chen, Chien-Ting; Ku, Tsun; Hsieh, Wen-Tai; Chiu, Hung-Sheng; Yang, Ren-Dar. (2012). "<a href="https://wikipediaquality.com/wiki/English-To-Traditional_Chinese_Cross-Lingual_Link_Discovery_in_Articles_with_Wikipedia_Corpus">English-To-Traditional Chinese Cross-Lingual Link Discovery in Articles with Wikipedia Corpus</a>".