scholarly journals RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application

2019 ◽  
Vol 59 (6) ◽  
pp. 2529-2537 ◽  
Author(s):  
Connor W. Coley ◽  
William H. Green ◽  
Klavs F. Jensen
Keyword(s):  
Template Extraction

Fault template extraction to assist operators during industrial alarm floods

2016 ◽  
Vol 50 ◽  
pp. 32-44 ◽  
Author(s):  
Sylvie Charbonnier ◽  
Nabil Bouchair ◽  
Philippe Gayet
Keyword(s):  
Template Extraction

scholarly journals Research and Implementation of a Method for Web Log Analysis Template Extraction

2019 ◽  
Vol 162 ◽  
pp. 673-681
Author(s):  
Zengchao Ni ◽  
Hongqi Liu ◽  
Yuanping Chen ◽  
Dengsheng Wu
Keyword(s):  
Log Analysis ◽  
Web Log ◽  
Template Extraction

Template-Based Model for Mongolian-Chinese Machine Translation

2016 ◽  
Vol 20 (6) ◽  
pp. 893-901
Author(s):  
Jing Wu ◽  
◽  
Hongxu Hou ◽  
Feilong Bao ◽  
Yupeng Jiang
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Combined System ◽  
Translation Model ◽  
Parallel Corpus ◽  
Address Data ◽  
Fuzzy Match ◽  
Novel Method ◽  
Extraction Model ◽  
Template Extraction

Mongolian and Chinese statistical machine translation (SMT) system has its limitation because of the complex Mongolian morphology, scarce resource of parallel corpus and the significant syntax differences. To address these problems, we propose a template-based machine translation (TBMT) system and combine it with the SMT system to achieve a better translation performance. The TBMT model we proposed includes a template extraction model and a template translation model. In the template extraction model, we present a novel method of aligning and abstracting static words from bilingual parallel corpus to extract templates automatically. In the template translation model, our specially designed method of filtering out the low quality matches can enhance the translation performance. Moreover, we apply lemmatization and Latinization to address data sparsity and do the fuzzy match. Experimentally, the coverage of TBMT system is over 50%. The combined SMT system translates all the other uncovered source sentences. The TBMT system outperforms the baselines of phrase-based and hierarchical phrase-based SMT systems for +3.08 and +1.40 BLEU points. The combined system of TBMT and SMT systems also performs better than the baselines of +2.49 and +0.81 BLEU points.

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