An Implementation of a Fast Threaded Nondeterministic LL (*) Parser Generator

Faculty Computer Science Year: 2015
Type of Publication: ZU Hosted Pages: 0975 – 8887
Authors:
Journal: International Journal of Computer Applications International Journal of Computer Applications Volume: 130
Keywords : , Implementation , , Fast Threaded Nondeterministic , , Parser Generator    
Abstract:
Parsers are used in many applications such as compilers, NLP and other applications. Parsers that are developed by hand are a complex task and require a generator to automatically generate the parser. The generator reads
   
     
 
       

Author Related Publications

  • Amr Mohammed Abdel Latif Emam, "DisBlue+: A distributed annotation-based C# compiler", Egyptian Informatics Journal, 2010 More
  • Amr Mohammed Abdel Latif Emam, "TGLL: A Fast Threaded Nondeterministic LL(*) Parsing", ARPN Journal of Systems and Softwar, 2015 More
  • Amr Mohammed Abdel Latif Emam, "CUDAQuat : new parallel framework for fast computation of quaternion moments for color images applications", Springer, 2021 More
  • Amr Mohammed Abdel Latif Emam, "Parallel Framework for Memory-Efficient Computation of Image Descriptors for Megapixel Images", Elsevier, 2023 More
  • Amr Mohammed Abdel Latif Emam, "Deep Learning Model for Fine-Grained Aspect-Based Opinion Mining", IEEE, 2020 More

Department Related Publications

  • Ahmed Raafat Abass Mohamed Saliem, "BERT-CNN: A Deep Learning Model for Detecting Emotions from Text", Tech Science Press, 2021 More
  • Ibrahiem Mahmoud Mohamed Elhenawy, "BERT-CNN: A Deep Learning Model for Detecting Emotions from Text", Tech Science Press, 2021 More
  • Ahmed Raafat Abass Mohamed Saliem, "Using General Regression with Local Tuning for Learning Mixture Models from Incomplete Data Sets", ScienceDirect, 2010 More
  • Ahmed Raafat Abass Mohamed Saliem, "On determining efficient finite mixture models with compact and essential components for clustering data", ScienceDirect, 2013 More
  • Ahmed Raafat Abass Mohamed Saliem, "Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data", ScienceDirect, 2012 More
Tweet