Fifty years of spellchecking

A short history of spellchecking from the late 1950s to the present day, describing its development through dictionary lookup, affix stripping, correction, confusion sets, and edit distance to the use of gigantic databases

Ordering the suggestions of a spellchecker without using context.

Having located a misspelling, a spellchecker generally offers some suggestions for the intended word. Even without using context, a spellchecker can draw on various types of information in ordering its suggestions. A series of experiments is described, beginning with a basic corrector that implements a well-known algorithm for reversing single simple errors, and making successive enhancements to take account of substring matches, pronunciation, known error patterns, syllable structure and word frequency. The improvement in the ordering produced by each enhancement is measured on a large corpus of misspellings. The final version is tested on other corpora against a widely used commercial spellchecker and a research prototype

Does the MS Spell Checker Effectively Correct Non-Native English Writers’ Errors? A Case Study of Saudi University Students

Those learning English as a second or foreign language use spell checkers to correct the mistakes and errors they may have made while typing texts on a computer However scholars have debated the effectiveness of such checkers which were originally designed to fix the spelling mistakes of native speakers An example of these checkers is the Microsoft MS Word program which constitutes the focus of the current study This study examined how MS Word treats misspellings made by Saudi learners of English as a foreign language It specifically addressed three research questions 1 which L2 spelling errors were successfully fixed by MS Word 2 which L2 spelling errors were unsuccessfully fixed by MS Word and 3 how did intermediate L2 learners respond to alternative corrections provided by MS Word A screentracking software Screencast-O-Matic was used to monitor the MS Word spell checker s treatment of misspelled words It was also used to track learners reactions to alternative corrections provided by MS Word in real time The study analysed 401 errors made by25 female intermediate-level English learners at a Saudi universit

The development of an incremental debugging system : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University

Debugging is a major area of software development that has received little attention. This thesis starts by looking at work done in the area of bug prevention, bug detection, bug location and bug correction. A debugging system, BIAS, is proposed to help in detecting, locating and correcting bugs. Three major design goals are established. Firstly, the system should be simple and easy to understand as this will encourage use. Secondly, the system should be general so that it will be available to a large number of users. Finally, it should be incremental as this will save users' time. An incremental language, STILL, is designed to show how BIAS applies to structured languages. The construction of the system is shown. Each data structure, and how it is used, is described. BIAS uses an interpretive system and runs threaded code on a pseudo-machine. How the threads are interpreted and how they are set up is shown next. The use of BIAS is shown by following through an example session with the system. This consists of entering a program, editing it, and running it. As bugs show themselves, various debugging commands are used to locate the bugs. The program is then edited, and the corrections linked into the code so that it will run correctly. This cycle is repeated until no bugs remain, without at any time recompiling the whole program. It turns out that the best way of achieving the design goals is to extend an incremental compiler host to include debugging commands. This gives a clear emphasis to the power of incremental compilers

Automatic Correction of Arabic Dyslexic Text

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%

Automated Detection of Usage Errors in non-native English Writing

In an investigation of the use of a novelty detection algorithm for identifying inappropriate word combinations in a raw English corpus, we employ an unsupervised detection algorithm based on the one- class support vector machines (OC-SVMs) and extract sentences containing word sequences whose frequency of appearance is significantly low in native English writing. Combined with n-gram language models and document categorization techniques, the OC-SVM classifier assigns given sentences into two different groups; the sentences containing errors and those without errors. Accuracies are 79.30 % with bigram model, 86.63 % with trigram model, and 34.34 % with four-gram model