Difference between revisions of "Research on the Extraction of Wikipedia-Based Chinese-Khmer Named Entity Equivalents"
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+ | | title = Research on the Extraction of Wikipedia-Based Chinese-Khmer Named Entity Equivalents | ||
+ | | date = 2015 | ||
+ | | authors = [[Qing Xia]]<br />[[Xin Yan]]<br />[[Zhengtao Yu]]<br />[[Shengxiang Gao]] | ||
+ | | doi = 10.1007/978-3-319-25207-0_32 | ||
+ | | link = http://dl.acm.org/citation.cfm?id=2978858 | ||
+ | }} | ||
'''Research on the Extraction of Wikipedia-Based Chinese-Khmer Named Entity Equivalents''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Qing Xia]], [[Xin Yan]], [[Zhengtao Yu]] and [[Shengxiang Gao]]. | '''Research on the Extraction of Wikipedia-Based Chinese-Khmer Named Entity Equivalents''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Qing Xia]], [[Xin Yan]], [[Zhengtao Yu]] and [[Shengxiang Gao]]. | ||
== Overview == | == Overview == | ||
Named entity equivalent has been playing a significant role in the processing of cross-language information. However limited by the corpora resource, few in-depth studies have been made on the extraction of the bilingual Chinese-Khmer [[named entity]] equivalents. On account of this, this paper proposes a [[Wikipedia]]-based approach, utilizes the internal web links in Wikipedia and computes the feature similarity to extract the bilingual Chinese-Khmer named entity equivalents. The experimental result shows that good effect has been achieved when the entity equivalents are acquired through the internal web links in Wikipedia with F value up to 90.67%. Also it shows that the result is quite favorable when the bilingual Chinese-Khmer named entity equivalents are acquired through the computation of feature similarity, turning out that the method proposed in this paper is able to give better effect. | Named entity equivalent has been playing a significant role in the processing of cross-language information. However limited by the corpora resource, few in-depth studies have been made on the extraction of the bilingual Chinese-Khmer [[named entity]] equivalents. On account of this, this paper proposes a [[Wikipedia]]-based approach, utilizes the internal web links in Wikipedia and computes the feature similarity to extract the bilingual Chinese-Khmer named entity equivalents. The experimental result shows that good effect has been achieved when the entity equivalents are acquired through the internal web links in Wikipedia with F value up to 90.67%. Also it shows that the result is quite favorable when the bilingual Chinese-Khmer named entity equivalents are acquired through the computation of feature similarity, turning out that the method proposed in this paper is able to give better effect. |
Revision as of 08:59, 14 August 2020
Authors | Qing Xia Xin Yan Zhengtao Yu Shengxiang Gao |
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Publication date | 2015 |
DOI | 10.1007/978-3-319-25207-0_32 |
Links | Original |
Research on the Extraction of Wikipedia-Based Chinese-Khmer Named Entity Equivalents - scientific work related to Wikipedia quality published in 2015, written by Qing Xia, Xin Yan, Zhengtao Yu and Shengxiang Gao.
Overview
Named entity equivalent has been playing a significant role in the processing of cross-language information. However limited by the corpora resource, few in-depth studies have been made on the extraction of the bilingual Chinese-Khmer named entity equivalents. On account of this, this paper proposes a Wikipedia-based approach, utilizes the internal web links in Wikipedia and computes the feature similarity to extract the bilingual Chinese-Khmer named entity equivalents. The experimental result shows that good effect has been achieved when the entity equivalents are acquired through the internal web links in Wikipedia with F value up to 90.67%. Also it shows that the result is quite favorable when the bilingual Chinese-Khmer named entity equivalents are acquired through the computation of feature similarity, turning out that the method proposed in this paper is able to give better effect.