Leveraging Parallel Corpora and Existing Wordnets for Automatic Construction of the Slovene Wordnet

Author(s):  
Darja Fišer
Informatica ◽  
2018 ◽  
Vol 29 (4) ◽  
pp. 693-710
Author(s):  
Algirdas Laukaitis ◽  
Darius Plikynas ◽  
Egidijus Ostasius

2016 ◽  
Vol 36 (1) ◽  
pp. 147
Author(s):  
Beatriz Sánchez Cárdenas ◽  
Pamela Faber

http://dx.doi.org/10.5007/2175-7968.2016v36nesp1p147Research in terminology has traditionally focused on nouns. Considerably less attention has been paid to other grammatical categories such as adverbs. However, these words can also be problematic for the novice translator, who tends to use the translation correspondences in bilingual dictionaries without realizing that formal equivalence is not necessarily the same as textual equivalence. However, semantic values, acquired in context, go far beyond dictionary meaning and are related to phenomena such as semantic prosody and preferences of lexical selection that can vary, depending on text type and specialized domain.This research explored the reasons why certain adverbial discourse connectors, apparently easy to translate, are a source of translation problems that cannot be easily resolved with a bilingual dictionary. Moreover, this study analyzed the use of parallel corpora in the translation classroom and how it can increase the quality of text production. For this purpose, we compared student translations before and after receiving training on the use of corpus analysis tools


2020 ◽  
Vol 10 (11) ◽  
pp. 3904
Author(s):  
Van-Hai Vu ◽  
Quang-Phuoc Nguyen ◽  
Joon-Choul Shin ◽  
Cheol-Young Ock

Machine translation (MT) has recently attracted much research on various advanced techniques (i.e., statistical-based and deep learning-based) and achieved great results for popular languages. However, the research on it involving low-resource languages such as Korean often suffer from the lack of openly available bilingual language resources. In this research, we built the open extensive parallel corpora for training MT models, named Ulsan parallel corpora (UPC). Currently, UPC contains two parallel corpora consisting of Korean-English and Korean-Vietnamese datasets. The Korean-English dataset has over 969 thousand sentence pairs, and the Korean-Vietnamese parallel corpus consists of over 412 thousand sentence pairs. Furthermore, the high rate of homographs of Korean causes an ambiguous word issue in MT. To address this problem, we developed a powerful word-sense annotation system based on a combination of sub-word conditional probability and knowledge-based methods, named UTagger. We applied UTagger to UPC and used these corpora to train both statistical-based and deep learning-based neural MT systems. The experimental results demonstrated that using UPC, high-quality MT systems (in terms of the Bi-Lingual Evaluation Understudy (BLEU) and Translation Error Rate (TER) score) can be built. Both UPC and UTagger are available for free download and usage.


2017 ◽  
Vol 20 (2) ◽  
pp. 289-296 ◽  
Author(s):  
Ali Benabdallah ◽  
Mohammed AlaEddine Abderrahim ◽  
Mohammed El-Amine Abderrahim

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