Re-ranking for Bilingual Lexicon Extraction with Bi-directional Linear Transformation from Comparable Corpora

2016 ◽  
pp. 25-34
Author(s):  
Chunyue Zhang ◽  
Tiejun Zhao
Keyword(s):  
Linear Transformation ◽  
Comparable Corpora ◽  
Bilingual Lexicon

Improving Bilingual Lexicon Extraction Performance from Comparable Corpora via Optimizing Translation Candidate Lists

2014 ◽  
Author(s):  
Shaoqi Wang ◽  
Miao Li ◽  
Zede Zhu ◽  
Zhenxin Yang ◽  
Shizhuang Weng
Keyword(s):  
Comparable Corpora ◽  
Bilingual Lexicon ◽  
Extraction Performance

scholarly journals Iterative bilingual lexicon extraction from comparable corpora using a modified perceptron algorithm

2014 ◽  
Vol 7 ◽  
pp. 1335-1343
Author(s):  
Hong-Seok Kwon ◽  
Hyeong-Won Seo ◽  
Minah Cheon ◽  
Jae-Hoon Kim
Keyword(s):  
Comparable Corpora ◽  
Bilingual Lexicon ◽  
Perceptron Algorithm

scholarly journals A geometric view on bilingual lexicon extraction from comparable corpora

2004 ◽  
Author(s):  
E. Gaussier ◽  
J.-M. Renders ◽  
I. Matveeva ◽  
C. Goutte ◽  
H. Déjean
Keyword(s):  
Comparable Corpora ◽  
Bilingual Lexicon

scholarly journals Exploiting Comparable Corpora to Enhance Bilingual Lexicon Induction from Monolingual Corpora

2020 ◽  
Vol 13 (5) ◽  
pp. 379-392
Author(s):  
Rizka Sholikah ◽  
◽  
Yasuhiko Morimoto ◽  
Agus Arifin ◽  
Chastine Fatichah ◽  
...  
Keyword(s):  
Comparable Corpora ◽  
Bilingual Lexicon

Measuring bilingual corpus comparability

2018 ◽  
Vol 24 (4) ◽  
pp. 523-549 ◽  
Author(s):  
BO LI ◽  
ERIC GAUSSIER ◽  
DAN YANG
Keyword(s):  
Real World ◽  
Gold Standard ◽  
The Real ◽  
Comparable Corpora ◽  
Bilingual Dictionary ◽  
Bilingual Lexicon ◽  
Real World Application ◽  
Parallel Resources

AbstractComparable corpora serve as an important substitute for parallel resources in cases of under-resourced language pairs. Previous work mostly aims to find a better strategy to exploit existing comparable corpora, while ignoring the variety in corpus quality. The quality of comparable corpora affects a lot its usability in practice, a fact that has been justified by several studies. However, researchers have not been able to establish a widely accepted and fully validated framework to measure corpus quality. We will thus investigate in this paper a comprehensive methodology to deal with the quality of comparable corpora. To be exact, we will propose several comparability measures and a quantitative strategy to test those measures. Our experiments show that the proposed comparability measure can capture gold-standard comparability levels very well and is robust to the bilingual dictionary used. Moreover, we will show in the task of bilingual lexicon extraction that the proposed measure correlates well with the performance of the real world application.

Exploiting unbalanced specialized comparable corpora for bilingual lexicon extraction

2016 ◽  
Vol 22 (4) ◽  
pp. 575-601 ◽  
Author(s):  
EMMANUEL MORIN ◽  
AMIR HAZEM
Keyword(s):  
Projection Method ◽  
Historical Context ◽  
Terminology Extraction ◽  
Comparable Corpora ◽  
Bilingual Lexicon ◽  
Main Work ◽  
Prediction Techniques

AbstractThe main work in bilingual lexicon extraction from comparable corpora is based on the implicit hypothesis that corpora are balanced in terms of size. However, the historical context-based projection method is relatively insensitive to the size of each part of the comparable corpus. Within this context, we have carried out a study on the influence of unbalanced specialized comparable corpora and on the quality of bilingual terminology extraction by doing different experiments. Moreover, we have introduced a strategy into the context-based projection method to re-estimate word co-occurrence observations. This is done by using smoothing or prediction techniques that boost the observations of word co-occurrences which are mainly useful for the smallest part of an unbalanced comparable corpus. Our results show that the use of unbalanced specialized comparable corpora results in a significant improvement in the quality of extracted lexicons.

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