scholarly journals Automatic Correction of Arabic Dyslexic Text

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 19 ◽  
Author(s):  
Maha Alamri ◽  
William Teahan
Keyword(s):  
Word Processing ◽  
Language Model ◽  
Text Compression ◽  
Arabic Text ◽  
Automatic Correction ◽  
Compression Scheme ◽  
Candidate List ◽  
Arabic Word ◽  
Processing Software ◽  
Correction System

This paper proposes an automatic correction system that detects and corrects dyslexic errors in Arabic text. The system uses a language model based on the Prediction by Partial Matching (PPM) text compression scheme that generates possible alternatives for each misspelled word. Furthermore, the generated candidate list is based on edit operations (insertion, deletion, substitution and transposition), and the correct alternative for each misspelled word is chosen on the basis of the compression codelength of the trigram. The system is compared with widely-used Arabic word processing software and the Farasa tool. The system provided good results compared with the other tools, with a recall of 43%, precision 89%, F1 58% and accuracy 81%.

scholarly journals Lossless text compression using GPT-2 language model and Huffman coding

2021 ◽  
Vol 102 ◽  
pp. 04013
Author(s):  
Md. Atiqur Rahman ◽  
Mohamed Hamada
Keyword(s):  
Data Compression ◽  
State Of The Art ◽  
Language Model ◽  
Huffman Coding ◽  
Original Text ◽  
Text Compression ◽  
Compression Technique ◽  
Daily Life Activities ◽  
Burrows Wheeler Transform ◽  
Compressed Data

Modern daily life activities produced lots of information for the advancement of telecommunication. It is a challenging issue to store them on a digital device or transmit it over the Internet, leading to the necessity for data compression. Thus, research on data compression to solve the issue has become a topic of great interest to researchers. Moreover, the size of compressed data is generally smaller than its original. As a result, data compression saves storage and increases transmission speed. In this article, we propose a text compression technique using GPT-2 language model and Huffman coding. In this proposed method, Burrows-Wheeler transform and a list of keys are used to reduce the original text file’s length. Finally, we apply GPT-2 language mode and then Huffman coding for encoding. This proposed method is compared with the state-of-the-art techniques used for text compression. Finally, we show that the proposed method demonstrates a gain in compression ratio compared to the other state-of-the-art methods.

Using Technology to Examine the Entity-Choice Decision

2000 ◽  
Vol 22 (2) ◽  
pp. 76-99
Author(s):  
Nancy B. Nichols ◽  
Stephanie M. Bryant
Keyword(s):  
Cash Flow ◽  
Word Processing ◽  
Publicly Traded ◽  
Cd Rom ◽  
Technological Tools ◽  
Choice Decision ◽  
Processing Software ◽  
Tax Research

The AICPA Model Tax Curriculum (AICPA 1996a) stresses the importance of entity taxation throughout the first two undergraduate tax courses and a master's of accounting program. This paper provides instructors with a case that uses a simplified example of the Boston Celtics, a publicly traded partnership, to highlight the tax and nontax considerations that must be evaluated when making a choice of entity decision. The case also incorporates technological tools including CD-ROM or Internet-based tax research, Excel spreadsheets for preparing tax and cash flow projections, word-processing software, and an optional PowerPoint presentation. The case is designed to be completed in stages throughout the semester.

Software Use Through Monadic and Dyadic Procedure

2008 ◽  
pp. 2073-2089
Author(s):  
Gregory E. Truman
Keyword(s):  
Software Design ◽  
Word Processing ◽  
User Performance ◽  
Theoretical Discussion ◽  
Design Features ◽  
Research Context ◽  
Processing Software ◽  
Performance Enhancing

Our research objectives are to provide a theoretical discussion on how software may impact user performance in ways contrary to designers’ intentions and users’ desires, and to empirically evaluate user performance impacts that derive from ostensibly performance-enhancing software features. We propose that dyadic procedure is associated with higher levels of user performance when compared to monadic procedure. Using word-processing software utilization as the research context, we test the proposition on data from 46 participants. Contrary to expectations, the results suggest that dyadic procedure may decrease the accuracy of users’ work. We conclude that software design features that are intended to improve user performance may have opposite effects, which raise questions about these features’ utility and desirability.

A hedonic study of network effects in the market for word processing software

2006 ◽  
Vol 41 (4) ◽  
pp. 747-763 ◽  
Author(s):  
Sujoy Chakravarty ◽  
Kutsal Dogan ◽  
Nels Tomlinson
Keyword(s):  
Word Processing ◽  
Network Effects ◽  
Processing Software

CD-ROM Applications in Education

1989 ◽  
Vol 18 (1) ◽  
pp. 37-41
Author(s):  
Stephen D. Bliss ◽  
M. Joy Gorence ◽  
Donald Haight
Keyword(s):  
Language Arts ◽  
Word Processing ◽  
Writing Skills ◽  
Global Studies ◽  
Research Information ◽  
Education Students ◽  
Research Topics ◽  
Cd Rom ◽  
Processing Software ◽  
Read Only Memory

The advent of computers in the classroom has changed the shape of education. Students now have the opportunity to utilize Compact Disc-Read Only Memory (CD-ROM) for research and compilation of information. Through the introduction of CD-ROM, students have been encouraged to research topics across the curriculum. The ease and availability of information through the CD-ROM has positively reinforced students to research topics for use in Global Studies, Science, Language Arts, and Computer courses. In addition, students have also been encouraged to utilize word processing software to correlate their research information and to present their topics in a more professional manner. The students use of the CD-ROM as a research tool, and word processing software for the final product, also seems to have had a positive affect on their organizational and writing skills.

Using AlKaatibTM: Pedagogical and Research Applications

1986 ◽  
Vol 20 (1) ◽  
pp. 29-43
Author(s):  
Dilworth B. Parkinson
Keyword(s):  
Word Processing ◽  
Short Review ◽  
Daily Basis ◽  
Macintosh Computer ◽  
Processing Software

This Article consists of a short review of the Arabic and Persian word processing software for the Macintosh computer called AlKaatibTM, as well as a presentation of some of the ways in which it is now being used as a pedagogical and research aid. It should be stated at the outset that although I did not personally develop this software, I was intimately involved with developing the fonts for it, and with testing it in its initial stages, thereby providing input that was incorporated into the present version. This article should therefore be viewed not so much as an outside review of the software, but as an expression by someone who uses it on a daily basis of what it has meant to him.

scholarly journals Learning from past mistakes: improving automatic speech recognition output via noisy-clean phrase context modeling

2019 ◽  
Vol 8 ◽  
Author(s):  
Prashanth Gurunath Shivakumar ◽  
Haoqi Li ◽  
Kevin Knight ◽  
Panayiotis Georgiou
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Language Model ◽  
Context Modeling ◽  
Short Term ◽  
Extensive Analysis ◽  
Network Language ◽  
Correction System

AbstractAutomatic speech recognition (ASR) systems often make unrecoverable errors due to subsystem pruning (acoustic, language and pronunciation models); for example, pruning words due to acoustics using short-term context, prior to rescoring with long-term context based on linguistics. In this work, we model ASR as a phrase-based noisy transformation channel and propose an error correction system that can learn from the aggregate errors of all the independent modules constituting the ASR and attempt to invert those. The proposed system can exploit long-term context using a neural network language model and can better choose between existing ASR output possibilities as well as re-introduce previously pruned or unseen (Out-Of-Vocabulary) phrases. It provides corrections under poorly performing ASR conditions without degrading any accurate transcriptions; such corrections are greater on top of out-of-domain and mismatched data ASR. Our system consistently provides improvements over the baseline ASR, even when baseline is further optimized through Recurrent Neural Network (RNN) language model rescoring. This demonstrates that any ASR improvements can be exploited independently and that our proposed system can potentially still provide benefits on highly optimized ASR. Finally, we present an extensive analysis of the type of errors corrected by our system.

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