Korean spelling error correction using a Hangul similarity algorithm

Chinese Spelling Check (CSC) aims to detect and correct spelling errors in Chinese. Most CSC models rely on human-defined confusion sets to narrow the search space, failing to resolve errors outside the confusion set. However, most spelling errors in current benchmark datasets are character pairs in similar pronunciations. Errors in similar shapes and errors which are visually and phonologically irrelevant are not considered. Furthermore, widely-used automatically generated training data in CSC tasks leads to label leakage and unfair comparison between different methods. In this work, we propose a feature (visual and phonological) enhanced siamese BERT to (1) correct spelling errors without using confusion sets; (2) integrate phonological and visual features for CSC by a glyph graph; (3) improve performance for unseen spelling errors. To evaluate CSC methods fairly and comprehensively, we build a large-scale CSC dataset in which the number of samples in different error types is the same. The experimental results show that the proposed approach achieves better performance compared with previous state-of-the-art methods on three benchmark datasets and the new error-type balanced dataset.

Spell Correction is a widely referred problem in natural language processing. Since spelling errors prevent perfect transmission of the author's intended concepts to the audience, writers and researchers usually spend a lot of time reviewing, to detect and correct spelling errors in their writings. Therefore, an automatic tool would be a great help in this area. Persian language is very prone to these errors due to its unique features. Also, the introduction of Arabic sentences and terms into this language has increased the challenges in spelling correction. Thus, there is a need for a tool that can detect and correct spelling errors in bilingual Persian and Arabic content. In this work, a supervised deep learning-based approach is proposed which benefits from a conditional random field (CRF) recurrent neural network to correct bilingual Arabic and Persian spelling errors. In order to create a suitable data set for training and testing the model presented in the proposed approach, 220,000 sentences with Arabic and Persian content were taken. Then artificially and using the methods of producing correct and error pairs, spelling errors were generated. In the next step, using the neural network based on the conditional random field, a model was presented that takes the features extracted from the data set as input to the network and makes predictions. The design of these features is one of the important points in this type of implementation. The results of the evaluations show that the proposed approach has a good and acceptable accuracy as the first bilingual Arabic and Persian Spell Corrector.

Second language (L2) writing assessments seldom allow a spellchecker and often have a time limit. Naturally, test takers often submit responses with spelling errors. However, little is known about whether and how spelling errors in test taker responses affect trained raters’ scoring decisions. In this study, we investigated the impact of spelling errors on trained raters’ holistic evaluation of response quality. We selected 148 responses to four L2 writing tasks, and created error-free versions of the responses by correcting spelling errors in the original responses. Both the original and corrected responses were randomly assigned to trained raters, who scored the responses according to the same holistic scoring rubrics. We compared the resulting scores of original and corrected responses to gauge the impact of spelling errors. We also examined whether the impact of spelling errors varied across different task types and spelling error characteristics. The results showed that the spelling error correction led to an average of more than a half score point increase. The score gains from the error correction varied across task types and the quantity of corrected errors. These findings have multiple implications, including suggestions for rater training and assessment development.

Scientific writing requires precision and clarity to uphold credibility and effective communication. Errors such as spelling mistakes and typos can compromise the quality and reliability of scientific texts. This study proposes a Long Short-Term Memory (LSTM)-based approach to detect and correct spelling errors, enhancing text accuracy and readability. The dataset comprises 45,698 standard words, supplemented with typo variations to improve model performance. Data is sourced from the Indonesian Dictionary (KBBI) and undergoes normalization and preprocessing to capture diverse error patterns. The model’s performance is evaluated using a confusion matrix, achieving 93% accuracy and high precision, recall, and F1-score metrics. These results demonstrate that the proposed NLP-based LSTM model offers an effective and reliable solution for identifying and correcting spelling errors. This approach significantly enhances the quality of scientific writing, ensuring more transparent and credible communication.

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.

This paper proposes novel approaches for checking and correcting spelling errors in Sinhala essays written by candidates of grade five scholarship examination. They don't have a proper mechanism to identify their spelling mistakes in essays by themselves. Spelling errors by such students may occur due to the violation of spelling rules, missing or adding of letters, missing modifiers, inaccurate spelling in a similar structure, and similar sound letters]. To mitigate such challenges, the Sinhala corpus file has been developed to identify the accurate and inaccurate spellings of the written words. The role of this application is to identify the correct and incorrect words which are entered by the user and generate the most correct words as suggestions for the incorrect words. This paper introduces three new novel approaches to detect the correctly spelled words in Sinhala essays namely object word checker method, suffixes checker method and similar word checker method. With addition to that this paper discusses three approaches to generate accurate suggestions including one novel approach. When evaluating the accuracy of the spelling error detection and correction module the overall results for precision, recall, and the f -measure were recorded as 83.05%, 85.57%, and 86.62% respectively.

Rapid increase of the scale of text carries huge costs for manual proofreading. In comparison, automatic proofreading shows great advantages on time and human resource, drawing more researchers into it. In this paper, we propose two attention based deep neural network models combined with confusion sets to detect and correct possible Chinese spelling errors in character-level. Our proposed approaches first model the context of Chinese character embedding using Long Short-Term Memory (LSTM) networks, then score the probabilities of candidates from its confusion set through attention mechanism, choosing the highest one as the prediction answer. Also, we define a new methodology for obtaining (preceding text, following text, candidates, target) quads and provides a supervised dataset for training and testing (Our data has been released to the public in https://github.com/ccit-proofread.). Performance evaluation indicates that our models achieve the state-of-the-art performance and outperform a set of baselines.

The performance of the statistical context-sensitive spelling error correction depends on the quality and quantity of the data for statistical language model. In general, the size and quality of data in a statistical language model are proportional. However, as the amount of data increases, the processing speed becomes slower and storage space also takes up a lot. We suggest the improved statistical language model to solve this problem. And we propose an effective spelling error candidate generation method based on a new statistical language model. The proposed statistical model and the correction method based on it improve the performance of the spelling error correction and processing speed.

Detecting spelling errors and correcting those errors automatically is a great research challenge. Developing a precise spell checker for Bangla language which detects spelling errors and provides suggestions for correcting those errors, is quite difficult because of the complex rules of Bangla spelling. In this paper, a clustering-based spell checking technique is proposed for Bangla language that reduces both search space and search time. Therefore, it improves the performance of a spell checker. The proposed spell checker can handle both typographical errors and phonetic errors. To evaluate the proposed spell checking technique, we use 2,450 misspelled words and the result shows that the proposed approach performs better for checking and correcting spelling errors. The success rate of proposed spell checker is 99.8%. We compare our spell checking technique with two Bangla spell checkers, Avro and Puspa and the proposed system provides relatively better results.

The purpose of this review paper is to identify the core spelling errors in Arabic and Non Arabic Contexts. The most common difficulty that Arab learners may face during their English writing is correct spelling, for many different reasons such as the interference between English and Arabic language and the irregularity of the English language system. Several studies have been undertaken to evaluate writing mistakes and spelling errors in English, and most of them have classified spelling errors into three different categories: morphemic errors, where the errors occur in the morphemes parts (prefixes and suffixes); Intra-Morphemic errors, where errors occur in the word roots themselves such as deleting the final (silent) e vowel in the word write, and splits types, where the learners leave a space inside the word for example, write my self as two words instead of myself, one word. Apart from the three categories mentioned above, other studies claim that there are eight different types of error related to the abilities of the students and the nature of the error, and these include inversion, omission, substitution, segmentation, insertion, pronunciation, miscellaneous, and unclassified errors. In this review paper, we have found interlingual and intralingual –related errors where interlingual errors are mainly caused by the interference of the primary or mother language, while, intralingual errors are due to the system and instruction of the target language. Finally, suggestions are given based on previous research about how to review the spelling errors in Arabic and Non Arabic contexts to identify the error and also overcome the problem through alternatives that can be implemented to create a positive impact and can be furthermore used for all types of positive learning.

Unnatural text correction aims to automatically detect and correct spelling errors or adversarial perturbation errors in sentences. Existing methods typically rely on fine-tuning or adversarial training to correct errors, which have achieved significant success. However, these methods exhibit poor generalization performance due to the difference in data distribution between training data and real-world scenarios, known as the exposure bias problem. In this paper, we propose a self-correct adversarial training framework for learning from mistakes (LIMIT), which is a task- and model-independent framework to correct unnatural errors or mistakes. Specifically, we fully utilize errors generated by the model that are actively exposed during the inference phase, i.e., predictions that are inconsistent with the target. This training method not only simulates potential errors in real application scenarios, but also mitigates the exposure bias of the traditional training process. Meanwhile, we design a novel decoding intervention strategy to maintain semantic consistency. Extensive experimental results on Chinese unnatural text error correction datasets show that our proposed method can correct multiple forms of errors and outperforms the state-of-the-art text correction methods. In addition, extensive results on Chinese and English datasets validate that LIMIT can serve as a plug-and-play defense module and can extend to new models and datasets without further training.

In this paper we present the Arib system for Arabic spelling error detection and correction as part of the second Shared Task on Automatic Arabic Error Correction. Our system contains many components that address various types of spelling error and applies a combination of approaches including rule based, statistical based, and lexicon based in a cascade fashion. We also employed two core models, namely a probabilistic-based model and a distance-based model. Our results on the development and test set indicate that using the correction components in cascaded way yields the best results. The overall recall of our system is 0.51, with a precision of 0.67 and an F1 score of 0.58.

A sequence-to-sequence learning with neural networks has empirically proven to be an effective framework for Chinese Spelling Correction (CSC), which takes a sentence with some spelling errors as input and outputs the corrected one. However, CSC models may fail to correct spelling errors covered by the confusion sets, and also will encounter unseen ones. We propose a method, which continually identifies the weak spots of a model to generate more valuable training instances, and apply a task-specific pre-training strategy to enhance the model. The generated adversarial examples are gradually added to the training set. Experimental results show that such an adversarial training method combined with the pre-training strategy can improve both the generalization and robustness of multiple CSC models across three different datasets, achieving state-of-the-art performance for CSC task.

This paper presents the concept and technical justification of a software product for automatic detection and correction of spelling errors in German-language texts. The relevance of the topic is due to the complexity of the German language in terms of grammar, spelling and word formation, which creates significant difficulties for speakers of other languages. This is especially true for those who study the language or use it in professional activities, where the accuracy of speech is crucial. The developed concept is focused on identifying typical spelling errors, including omissions, letter permutations and incorrectly used words. Instead of the classic use of large dictionary databases and search methods by editing distance, the project involves working with machine learning data on typical errors, which allows reducing resource requirements and ensuring faster text processing. This approach makes the system more mobile, autonomous and suitable for integration into educational or professional environments. During the development process, a comprehensive system analysis was carried out: a goal tree was built, a context diagram was formed to describe information connections, the main stages of the system life cycle were determined, and the logic of the work was modeled using a UML activity diagram. Particular attention was paid to the choice of methods and means of implementation, including the analysis of software tools and Python libraries that best meet the tasks of automatic correction of spelling errors. The possibilities of integrating machine learning algorithms to improve the system's adaptation to new types of errors were considered. The results of the work form the basis for creating an effective, compact and adaptive tool that can be expanded in the future - in particular, to detect grammatical errors, which will significantly increase the functionality of the system and its practical value.

Working possible selves—currently activated images of an ideal or feared future self—have been argued to guide one’s motivation and actions. This study investigated how possible selves affect L2 learners’ motivational behaviors of persistence and effort as shown in essay revision and proofreading. One hundred and twelve ESL students were assigned to one of four conditions: successful future selves, unsuccessful future selves, successful past selves, or successful future of others. Participants were asked to imagine and freewrite about their assigned condition and to revise their essay until they were satisfied with the writing. Following this, participants proofread a reading text containing spelling errors. The time spent on revision was considered as an indicator of persistence, and the number of spelling error corrections was taken to represent effort. The analysis of group differences in performance revealed that the successful past selves group persisted longer in the revision task than the successful future selves group. No group differences were found in the proofreading task. The finding that the successful future selves group scored lowest of the four groups in the revision task contrasts sharply with results from previous research. Possible explanations are discussed in relation to the theory of possible selves and task characteristics.