The impact of spelling errors on trained raters’ scoring decisions

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.

Increasingly people use computers for word processing. This helps reduce word processing time and fatigue of hands, but may increase the possibility of occurrence of spelling errors. Although spelling errors are generally easy to find and correct, it is hard to make a document totally free of spelling errors partly due to lack of knowledge of users or presence of spelling errors which are difficult to notice. Since there is no set of online word processing rules and manners in place and problems of spelling errors are not often raised, spelling errors in important documents may lead to decrease in reliability. Even experts cannot correct spelling errors perfectly, so there is a need for research to come up with spelling correction methods for the general public. This study aims to correct spelling errors using Korean alphabet similarity algorithm. To this end, words most similar to misspelled words found in a corpus containing spelling errors collected by previous research were identified to correct spelling errors by measuring frequency of simultaneous appearance with adjacent words.

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.

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.

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.

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.

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.

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 spelling error is a mistake occurred while typing the text document. The applications like search engines, information retrieval, emails, etc., require user typing. In such applications, good spell-checker is essential to rectify the misspelling. Spell-checkers for western languages like English are very powerful and can handle any type of spelling errors, whereas in the case of Indian languages like Hindi, Urdu, Bengali, Kannada, Assamese, etc., the available spell-checkers are very basic ones. These spell-checkers are developed using traditional methods like statistical methods and rule-based methods. This article presents a novel model HINDIA to handle the spelling errors of the Hindi language, one of the most spoken languages in India. It utilizes a deep-learning method for spelling error detection and correction. The proposed spell-checking model works in two phases. In the first phase model identifies the erroneous words in the input sample and in the second phase it replaces the wrong words with the most probable correct words. Model HINDIA is developed using the attention-based encoder–decoder bidirectional recurrent neural network (BiRNN) which uses long short-term memory cells. Several modifications in the BiRNN have been made and network is fine-tuned to process the spelling errors of Hindi language. It uses publicly available dataset ‘monolingual corpus’ developed by IIT Mumbai for training and testing. The performance of the proposed model is evaluated in two scenarios. In the first scenario where the testing dataset is generated using split function. HINDIA performs significantly well with precision 0.86, recall 0.72, f-measure 0.78 and accuracy 0.80. Further, in the second scenario, where a dataset is manually generated its performance is fairly good with precision 0.81, recall 0.72, f-measure 0.76 and accuracy 0.74. Model HINDIA gives better performance than the deep-learning-based Malayalam spell-checker and some other deep-learning-based correction models present in the literature.

Research on tools for automating the proofreading of Arabic text has received much attention in recent years. There is an increasing demand for applications that can detect and correct Arabic spelling and grammatical errors to improve the quality of Arabic text content and application input. Our review of previous studies indicates that few Arabic spell-checking research efforts appropriately address the detection and correction of ill-formed words that do not conform to the Arabic morphology system. Even fewer systems address the detection and correction of erroneous well-formed Arabic words that are either contextually or semantically inconsistent within the text. We introduce an approach that investigates employing deep neural network technology for error detection in Arabic text. We have developed a systematic framework for spelling and grammar error detection, as well as correction at the word level, based on a bidirectional long short-term memory mechanism and word embedding, in which a polynomial network classifier is at the top of the system. To get conclusive results, we have developed the most significant gold standard annotated corpus to date, containing 15 million fully inflected Arabic words. The data were collected from diverse text sources and genres, in which every erroneous and ill-formed word has been annotated, validated, and manually revised by Arabic specialists. This valuable asset is available for the Arabic natural language processing research community. The experimental results confirm that our proposed system significantly outperforms the performance of Microsoft Word 2013 and Open Office Ayaspell 3.4, which have been used in the literature for evaluating similar research.

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.

This study investigated the impact of different lengths of pre-task planning time on performance in a test of second language speaking ability for university admission. In the study, 47 Turkish-speaking learners of English took a test of English language speaking ability. The participants were divided into two groups according to their language proficiency, which was estimated through a paper-based English placement test. They each completed four monologue tasks: two picture-based narrative tasks and two description tasks. In a balanced design, each test taker was allowed a different length of planning time before responding to each of the four tasks. The four planning conditions were 30 seconds, 1 minute, 5 minutes, and 10 minutes. Trained raters awarded scores to the test takers using an analytic rating scale and a context-specific, binary-choice rating scale, designed specifically for the study. The results of the rater scores were analysed by using a multifaceted Rasch measurement. The impact of pre-task planning on test scores was found to be influenced by four variables: the rating scale; the task type that test takers completed; the length of planning time provided; and the test takers’ levels of proficiency in the second language. Increases in scores were larger on the picture-based narrative tasks than on the two description tasks. The results also revealed a relationship between proficiency and pre-task planning, whereby statistical significance was only reached for the increases in the scores of the lowest-level test takers. Regarding the amount of planning time, the 5-minute planning condition led to the largest overall increases in scores. The research findings offer contributions to the study of pre-task planning and will be of particular interest to institutions seeking to assess the speaking ability of prospective students in English-medium educational environments.

This research, which considered context-sensitive spelling error correction as the classification problem of words along with the context as the solution of semantic ambiguity, has a limitation that correction can be corrected in a particular words pair. To overcome this, this research suggested a technique for detecting and correcting context-sensitive spelling error probabilistically, by selecting the whole eojeol1 as the target words and generating corresponding available candidate. The context-sensitive spelling error correction model suggested by this research is a model based on the noisy channel. It utilized eojeol n-gram model as a language model, and the context-sensitive spelling error rate, the channel probability, was utilized as an equipment for user adaptive context-sensitive spelling error detection and correction. This research composed an evaluation data by transforming eojeol per sentence from 2,000 correct sentences randomly extracted from Sejong corpus to error eojeol. The test result showed function, i.e. precision over 95%, and recall over 70%. This showed the detection and correction method suggested by this research could be applied in the extremely unpredictable error generating language environments.

H.M. is a 17-year-old girl of at least average intelligence and with an above-average oral vocabulary. She is impaired at non-word reading in comparison to word reading. She is incapable of reading any long nonsense words or long unusual regular words correctly. Many of her non-word reading responses are lexicalizations. She reads non-words homophonic with real words better than she reads non-words that are not homophonic with real words. Her reading age is 10 years 11 months. A large proportion of her reading errors are derivational or visual paralexias. She makes no semantic errors or errors of regularization and few neologistic responses. H.M. is not influenced by the dimensions of spelling-to-sound regularity and word length. There is no effect of word class on single-word reading although a function word deficit is present when continuous text is read aloud. H.M. is impaired at word reading when the stimulus items are distorted in a manner that reduces the potential for global perception. H.M.'s spelling errors are primarily phonological, though more complex errors are made than those characteristic of surface agraphics. All of these features are consistent with reported cases of acquired phonological dyslexia. H.M. may be confidently regarded as a developmental phonological dyslexic.