2019/03/18 by Amr Adel Helmy, Helmy, Amr Adel
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #Machine Learning (cs.LG) #Natural Language Processing Techniques #Speech Recognition and Synthesis #Text and Document Classification Technologies #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.1903.07588
A dissertation submitted to the AASTMT on February 2019 in partial fulfillment of the requirements for the degree of Master of Science in Computer Science. arXiv admin note: text overlap with arXiv:1807.10854 by other authors without attribution
arxiv created 2019/03/18 · openalex publication_date 2019/03/18 · arxiv updated 2019/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This thesis presents a language-independent text classification model by introduced two new encoding methods "BUNOW" and "BUNOC" used for feeding the raw text data into a new CNN spatial architecture with vertical and horizontal convolutional process instead of commonly used methods like one hot vector or word representation (i.e. word2vec) with temporal CNN architecture. The proposed model can be classified as hybrid word-character model in its work methodology because it consumes less memory space by using a fewer neural network parameters as in character level representation, in addition to providing much faster computations with fewer network layers depth, as in word level representation. A promising result achieved compared to state of art models in two different morphological benchmarked dataset one for Arabic language and one for English language.