Frequency Effects on Spelling Error Recognition: An ERP Study

Spelling errors are ubiquitous in all writing systems. Most studies exploring spelling errors focused on the phonological plausibility of errors. However, unlike typical pseudohomophones, spelling errors occur in naturally produced written language with variable frequencies. We investigated the time course of recognition of the most frequent orthographic errors in Russian (error in an unstressed vowel at the root) and the effect of word frequency on this process. During ERP recording, 26 native Russian speakers silently read high-frequency correctly spelled words, low-frequency correctly spelled words, high-frequency words with errors, and low-frequency words with errors. The amplitude of P200 was more positive for correctly spelled words than for misspelled words and did not depend on the frequency of the words. Word frequency affected spelling recognition in the later stages of word processing (350-700 ms): high-frequency misspelled words elicited a greater P300 than high-frequency correctly spelled words, and low-frequency misspelled words elicited a greater N400 than low-frequency correctly spelled words. We observe spelling effects in the same time window for both the P300 and N400, which may reflect temporal overlap between mainly categorization processes based on orthographic properties for high-frequency words and phonological processes for low-frequency words. We concluded that two independent pathways can be active simultaneously during spelling recognition: one reflects mainly orthographic processing of high-frequency words and the other is the phonological processing of low-frequency words. Our findings suggest that these pathways are associated with different ERP components. Therefore, our results complement existing reading models and demonstrate that the neuronal underpinnings of spelling error recognition during reading depend on word frequency.

Analysis and Development of KEBI 1.0 Checker Framework as an Application of Indonesian Spelling Error Detection

At educational institutions, especially at University, writing scientific papers is a skill that must be possessed by academics such as educators and students. However, writing scientific papers is not easy, there are many provisions and rules that need to be fulfilled. Several studies show that there are still many academics who make mistakes in writing their scientific papers. Some of the mistakes made include punctuation errors, typographic writing errors and the use of non-standard words in Indonesian. Researchers in Indonesia have developed various spelling error detection applications in Indonesian-language scientific papers. This study tries to analyze the development of an application framework for detecting Indonesian spelling errors from various assessment indicators. This study tries to compare the application framework for detecting spelling errors between other studies with proposed application that named KEBI 1.0 Checker. KEBI 1.0 Checker as a spelling error detection application has 3 main features, namely detecting errors in the use of punctuation marks, writing typography, and using non-standard words in accordance with the standards of the Big Indonesian Dictionary and the General Guidelines for Indonesian Spelling. In addition, this study tries to objectively examine the complexity of the features, advantages and disadvantages, methods and the level of accuracy of each application. The results of the analysis show that KEBI 1.0 Checker has the completeness of features, fast computation time, easy application access, and an attractive user interface. However, it is still necessary to improve the precision in correcting spelling errors in typographic words.

Indonesian Spelling Error Detection and Type Identification Using Bigram Vector and Minimum Edit Distance Based Probabilities

Spelling error has become an error that is often found in this era which can be seen from the use of words that tend to follow trends or culture, especially in the younger generation. This study aims to develop and test a detection and identification model using a combination of Bigram Vector and Minimum Edit Distance Based Probabilities. Correct words from error words are obtained using candidates search and probability calculations that adopt the concept of minimum edit distance. The detection results then identified the error type into three types of errors, namely vowels, consonants and diphthongs from the error side on the tendency of the characters used as a result of phonemic rendering at the time of writing. The results of error detection and identification of error types obtained are quite good where most of the error test data can be detected and identified according to the type of error, although there are several detection errors by obtaining more than one correct word as a result of the same probability value of these words.

Corrigendum to: Combined Preference Ranking Algorithm for Comparing and Initial Ranking of Cloud Services

A spelling error appeared in the authors’ list of the article titled “Combined Preference Ranking Algorithm for Comparing and Initial Ranking of Cloud Services”, published in “Recent Advances in Electrical and Electronic Engineering”, 2020, Vol. 13, no. 2. [1]. Details of the error and a correction are provided here. We regret the error and apologize to readers. The original article can be found online at: https://benthamscience.com/journals/recent-advances-in-electrical-andelectronic- engineering/volume/13/issue/2/ ORIGINAL: Abdul Qadir Md and Varadarajan Vijayakumar CORRECTED: Abdul Quadir Md and Varadarajan Vijayakumar

Granska API – an Online API for Grammar Checking and Other NLP Services

We present an online API to access a number of Natural Language Processing services developed at KTH. The services work on Swedish text. They include tokenization, part-of-speech tagging, shallow parsing, compound word analysis, word inflection, lemmatization, spelling error detection and correction, grammar checking, and more. The services can be accessed in several ways, including a RESTful interface, direct socket communication, and premade Web forms. The services are open to anyone. The source code is also freely available making it possible to set up another server or run the tools locally. We have also evaluated the performance of several of the services and compared them to other available systems. Both the precision and the recall for the Granska grammar checker are higher than for both Microsoft Word and Google Docs. The evaluation also shows that the recall is greatly improved when combining all the grammar checking services in the API, compared to any one method, and combining services is made easy by the API.

An Analysis of Students’ Spelling Errors in Writing at the 8th grade of SMPN 2 Kamang Magek

The aim of the research was to find out students’ spelling errors and the causes of spelling errors in writing. Writing spelling correctly is important in order to not make misunderstanding for the reader and writer. In field, researcher found some problem at related to students’ spelling errors. Most of students were not interested when they wrote spell correctly. Then, most of students were doubt in the letter that have same sound. The last was most of students often wrote Indonesian spelling in writing English that have similiar sound. The purposes of this research based on three research questions were to find out the kinds of error in spelling, the most common spelling error, and the causes of spelling error were made by students in writing. In this research, the researcher used mix method research by using descriptive design. The mix method was the combination of qualitative research and quantitative research. Quantitative research was used to answer the kind of students’ spelling error in writing and most common of error in writing. Qualitative research was used to know the causes of students’ spelling error in writing. The informants of this research was the students of VIII-1 class in SMP N 2 Kamang Magek. The total was 22 students. The data was got through documentation and interview. Documentation was used to get quantitative data and interview was used to get qualitative data. The researcher asked the students’ writing to the teacher as a documentation. The interview was done directly and indirectly because of the pandemi. The result of the research showed that there were 184 spelling errors from 22 students. There were nine kinds of spelling error such as omission of letter, addition of letter, single letter instead of double letter, double letter instead of single letter, substitution of letter, interchange of two adjacent of letter, involving an apostrophe, multiple error. Therefore, the most common spelling error was substitution of letter which students changed letter incorrectly with 35,3% percentage. Moreover, there were some reasons why the students of SMP N 2 Kamang Magek made spelling error. The first was the difficulties between spoken and written form in English, it made students were not able to remember the spelling. Second was most of students were influenced by students’ first language when they wrote spelling. The last was lack of students’ desire to write correct spelling.

KEBI 1.0: Indonesian Spelling Error Detection System for Scientific Papers using Dictionary Lookup and Peter Norvig Spelling Corrector

Many Indonesian spelling errors occur in research papers published to the public, closely related to academics in all institutions such as research institutions, government, schools, and universities. The spelling errors usually writing punctuation, writing letters, writing words, writing words originating from foreign or regional languages (uptake words), using affixed words, and writing ineffective sentences. The mistakes made by the academics then become a cycle in the academic environment. They usually provide guidance for writing an undergraduate thesis, thesis, dissertations to students, or the other forms of documents and scientific papers. Therefore, the research proposed the application to facilitate all authors of scientific papers in producing quality scientific works based on the General Guidelines for Indonesian Spelling published by the Agency for Development and Language Development. The application is named KEBI 1.0 Checker (Indonesian Spelling Error 1.0 Checker), a web-based application with a built-in algorithm to detect and correct Indonesian Spelling in scientific papers. The experiment result shows that the application has given the best accuracy performance to correct the non-standard words, and typographical errors reached 100% and 55,52%, respectively. The application also has been detected 209 meaningless words. The application processing time is relatively low, the average time needed to correct non-standard words is 0.016 seconds, and typo words are 14.58 seconds. KEBI 1.0 Checker is helpful for the end-user in academics but needs to improve the vocabulary of the large corpus in various fields of science for correcting typo words.