Spelling Error Detection Research Articles

An Automated Word Spelling Error Detection System

Accurate spelling has extensive consequences for written communication's clarity, credibility, and general effectiveness in several contexts, including academic and professional writing and personal communications. The frequency of spelling errors has increased significantly recently, mainly owing to the widespread use of digital media and society's dependence on written communication. Misspelled words can diminish the reliability of written correspondence and result in misinterpretations. Hence, there is an urgent requirement for an innovative and advanced solution to improve the identification of spelling errors. This research seeks to address these challenges and develop a system that significantly enhances the quality of written communication by improving the accuracy and relevance of spelling error detection and correction suggestions. According to this study, the implemented model is a promising technique to correct spelling problems in legal terminology, has been praised for its effectiveness and usability. Its recommendations include continuous research and development, improving its ability to use contextual cues and domain-specific information for error correction, emphasizing user-centric customization, implementing collaborative feedback systems, offering extensive training and support, prioritizing ethical and regulatory considerations, and fostering interdisciplinary collaboration. These suggestions aim to optimize the Spello model's performance, resilience, and scalability, and make it a fundamental technology in promoting accuracy, integrity, and accessibility in the legal profession and society. The model can become a fundamental technology in promoting accuracy, integrity, and accessibility in the legal profession and society through combined efforts and collaboration.

An Unsupervised Domain-Adaptive Framework for Chinese Spelling Checking

Chinese Spelling Check (CSC) is a meaningful task in the area of Natural Language Processing (NLP), which aims at detecting spelling errors in Chinese texts and then correcting these errors. Current typical Chinese Spelling Check models have shown impressive performance in general datasets with the help of pretrained language models such as BERT, but suffer great perform loss in downstream tasks with domain-specific terms because they are primarily trained on general corpora. To verify the cross-domain adaptation ability of these models, we build three new datasets with abundant domain-specific terms on financial, medical, and legal domains and conduct empirical investigations on them in the corresponding domain-specific test datasets to verify the cross-domain adaptation ability. In response to the poor performance of the existing models, we propose a framework named uChecker which utilizes unsupervised method in spelling error detection and correction. Experiment results prove that uChecker can perform well in domain-specific test datasets while not losing its performance in the general domain.

Improving the quality of Persian clinical text with a novel spelling correction system

BackgroundThe accuracy of spelling in Electronic Health Records (EHRs) is a critical factor for efficient clinical care, research, and ensuring patient safety. The Persian language, with its abundant vocabulary and complex characteristics, poses unique challenges for real-word error correction. This research aimed to develop an innovative approach for detecting and correcting spelling errors in Persian clinical text.MethodsOur strategy employs a state-of-the-art pre-trained model that has been meticulously fine-tuned specifically for the task of spelling correction in the Persian clinical domain. This model is complemented by an innovative orthographic similarity matching algorithm, PERTO, which uses visual similarity of characters for ranking correction candidates.ResultsThe evaluation of our approach demonstrated its robustness and precision in detecting and rectifying word errors in Persian clinical text. In terms of non-word error correction, our model achieved an F1-Score of 90.0% when the PERTO algorithm was employed. For real-word error detection, our model demonstrated its highest performance, achieving an F1-Score of 90.6%. Furthermore, the model reached its highest F1-Score of 91.5% for real-word error correction when the PERTO algorithm was employed.ConclusionsDespite certain limitations, our method represents a substantial advancement in the field of spelling error detection and correction for Persian clinical text. By effectively addressing the unique challenges posed by the Persian language, our approach paves the way for more accurate and efficient clinical documentation, contributing to improved patient care and safety. Future research could explore its use in other areas of the Persian medical domain, enhancing its impact and utility.

Study’s system: a three-stage system for children handwriting recognition and spelling error detection

Study’s system: a three-stage system for children handwriting recognition and spelling error detection

Multi-head Attention Based Bidirectional LSTM for Spelling Error Detection in the Indonesian Language

Multi-head Attention Based Bidirectional LSTM for Spelling Error Detection in the Indonesian Language

Deep learning-based Turkish spelling error detection with a multi-class false positive reduction model

Deep learning-based Turkish spelling error detection with a multi-class false positive reduction model

Automatic spelling error detection and correction for Tigrigna information retrieval: a hybrid approach

This paper proposes a hybrid approach to design and implement query spelling error detection and correction (SEDC) for Tigrigna information retrieval (IR). Our approach, which is the main contribution to this work, is fast and robust to achieve better performance and also helps the users to easily insert their corrected queries to retrieve relevant information from the IR. This is achieved by combining the normalized measure of bigram overlap using the Jaccard coefficient (J.C) technique, a dynamic programming algorithm for edit distance, and probability of occurrence, which were used to make suggestions for the misspelt words. Our approach was evaluated on the SEDC subtasks separately. It achieved an F-measure of 98.85% on the spelling error detection subtask and an accuracy of 95.36% on the spelling error correction subtask. Thus, a comparison was conducted between our approach and the existing Tigrigna spell checker. It is found that our approach outperformed the existing spell checker and shows a 5.36% improvement in accuracy. This is by far the most promising result with regard to correcting the misspelt users’ queries and improving the overall performance of the IR.

Suggestion Generation for Erroneous Words in Tamil Documents Using N-gram Technique

A spell checker is a tool that finds and corrects erroneous words and grammatical mistakes in a text document. Spelling error detection and correction techniques are widely used by text editing systems, search engines, text to speech and speech to text conversion systems, machine translation systems and optical character recognition systems. The spell checkers for European languages and some Indic languages are well developed. However, perhaps, owing to Tamil being a morphologically rich and agglutinative language this has been a challenging task. Erroneous words and grammatical mistakes can occur in sentences due to various reasons. Erroneous words can be classified into two categories, namely non-word errors and real-word errors. This work aims to correct non-word errors in Tamil documents by suggesting alternatives. The proposed approach uses letter-level and word-level n-gram, stemming and hash table techniques. Test results show that the suggestions generated by the system are with 95% accuracy.

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.

A hybrid model for spelling error detection and correction for Urdu language

A hybrid model for spelling error detection and correction for Urdu language

Chinese Spelling Error Detection Using a Fusion Lattice LSTM

Spelling error detection serves as a crucial preprocessing in many natural language processing applications. Unlike English, where every single word is directly typed by keyboard, we have to use an input method to input Chinese characters. The pinyin input method is the most widely used. By intuition, pinyin should be helpful in detecting spelling errors. However, when detect spelling errors, most of the current methods ignore the pinyin information and adopt a pipeline framework that leads to error propagation. In this article, we propose a fusion lattice-LSTM model under the end-to-end framework to integrate character, word, and pinyin features for error detection. Experiments on the SIGHAN Bake-off-2015 dataset show that pinyin is a discriminating feature, and our end-to-end model outperforms the baseline models obviously.

Sentence Level N-Gram Context Feature in Real-Word Spelling Error Detection and Correction: Unsupervised Corpus Based Approach

Spell checking is the process of finding misspelled words and possibly correcting them. Most of the modern commercial spell checkers use a straightforward approach to finding misspellings, which considered a word is erroneous when it is not found in the dictionary. However, this approach is not able to check the correctness of words in their context and this is called real-word spelling error. To solve this issue, in the state-of-the-art researchers use context feature at fixed size n-gram (i.e. tri-gram) and this reduces the effectiveness of model due to limited feature. In this paper, we address the problem of this issue by adopting sentence level n-gram feature for real-word spelling error detection and correction. In this technique, all possible word n-grams are used to learn proposed model about properties of target language and this enhance its effectiveness. In this investigation, the only corpus required to training proposed model is unsupervised corpus (or raw text) and this enables the model flexible to be adoptable for any natural languages. But, for demonstration purpose we adopt under-resourced languages such as Amharic, Afaan Oromo and Tigrigna. The model has been evaluated in terms of Recall, Precision, F-measure and a comparison with literature was made (i.e. fixed n-gram context feature) to assess if the technique used performs as good. The experimental result indicates proposed model with sentence level n-gram context feature achieves a better result: for real-word error detection and correction achieves an average F-measure of 90.03%, 85.95%, and 84.24% for Amharic, Afaan Oromo and Tigrigna respectively. Keywords: S entence level n-gram, real-word spelling error, spell checker , unsupervised corpus based spell checker DOI: 10.7176/JIEA/10-4-02 Publication date: September 30 th 2020

HINDIA: a deep-learning-based model for spell-checking of Hindi language

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.

Deep Learning for Arabic Error Detection and Correction

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.

Detecting spelling errors in compound and pseudocompound words.

Three experiments using a spelling error detection task investigated the extent to which morphemes and pseudomorphemes affect word processing. We compared the processing of transparent compound words (e.g., doorbell), pseudocompound words (e.g., carpet), and matched control words (e.g., tomato). In half of the compound and pseudocompound words, spelling errors were created by transposing adjacent letters and in half of the control words, errors were created by transposing letters at the same location as the matched compound or pseudocompound words. Correctly spelled compound words were more easily processed than matched control words, but this advantage was removed when letter transpositions were introduced at the morpheme boundary. In contrast, misspelled pseudocompound words showed a processing deficit relative to their matched control words when letter transpositions were introduced at the (pseudo)morpheme boundary. The results strongly suggest that morphological processing is attempted obligatorily when the orthography indicates that morphological structure is present. However, the outcomes of the morphological processing attempts are different for compounds and pseudocompounds, as might be expected, given that only the compounds have a morphological structure that matches the structure suggested by the orthography. The findings reflect 2 effects: an orthographic effect that is facilitatory and not sensitive to morphological structure of the whole word, and a morphemic effect that is facilitatory for compounds but inhibitory for pseudocompounds. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Spell checker for consumer language (CSpell).

Automated understanding of consumer health inquiries might be hindered by misspellings. To detect and correct various types of spelling errors in consumer health questions, we developed a distributable spell-checking tool, CSpell, that handles nonword errors, real-word errors, word boundary infractions, punctuation errors, and combinations of the above. We developed a novel approach of using dual embedding within Word2vec for context-dependent corrections. This technique was used in combination with dictionary-based corrections in a 2-stage ranking system. We also developed various splitters and handlers to correct word boundary infractions. All correction approaches are integrated to handle errors in consumer health questions. Our approach achieves an F1 score of 80.93% and 69.17% for spelling error detection and correction, respectively. The dual-embedding model shows a significant improvement (9.13%) in F1 score compared with the general practice of using cosine similarity with word vectors in Word2vec for context ranking. Our 2-stage ranking system shows a 4.94% improvement in F1 score compared with the best 1-stage ranking system. CSpell improves over the state of the art and provides near real-time automatic misspelling detection and correction in consumer health questions. The software and the CSpell test set are available at https://umlslex.nlm.nih.gov/cSpell.

N-GRAMS SOLUTION FOR ERROR DETECTION AND CORRECTION IN HINDI LANGUAGE

Hindi is the National language of India, which is still in its early stage of research and development regarding natural language processing applications in comparison to other languages like English, Chinese. Natural language processing is a field of Artificial Intelligence, which includes major tasks such as information retrieval, word segmentation, speech recognition, parsing, part of speech tagging, text classification, automatic text summarization etc. Spelling detection and correction in Hindi language is an important task of NLP which has not gotten sufficient attention till date. Spelling detection and correction for Indian languages such as Hindi is considered as a difficult task. Hindi Language is very different from English language in its phonetic properties and grammatical rules. Thus the existing techniques and methods that are being used to check the errors in English language can’t be used for Hindi Language. There are mainly two types of error: Non word error and real word error. Error detection for non-word error in Hindi language has been done but for real word error no work has been done till date. This paper focused on Real word spelling error detection and correction in Hindi text by using N Grams Model and Levensthein edit distance algorithm.

Spelling checking using conditional random fields with feature induction for secondary language learners

This paper presents a framework for Chinese spelling error detection and correction using conditional random fields (CRFs) with feature induction for secondary language learners. The trend of learning Chinese as second language is increasing recently. CRFs are adopted here as the model that models the global and local information to judge if the word is correct or not. Local features are usually considered to make the decision for intelligent systems. Herein, CRFs are one of the most used statistical approaches those which can adjust the corresponding weights for features to achieve near optimal results. This paper invested an automatic rule induction method to capture the hidden features for spellcheck in Chinese. Considering position information, the features are inducted by counting in the training corpus automatically. Therefore, the CRFs integrate the features and achieve approximated optimum by adjusting the weights corresponding to related features. From the experimental results, we can find the proposed method outperforms the traditional approaches and obtains improvement for finding the misspelled words and correcting them. It should be concluded, from what has been illustrated above that the proposed method is near to practice and useful for the learners who take Chinese as a secondary language.

Improving post-processing optical character recognition documents with Arabic language using spelling error detection and correction

The optical character recognition (OCR) is used to convert scanned documents into text. The resulted text need to be validated for correctness. The problem increased when working on Arabic text because of the complexity of Arabic language. This research aims to explore the ways of improving OCR spell checking effectiveness by proposing a post-processing Arabic OCR system based on three different approaches: Microsoft Office Word with Google online suggestion system, Ayaspell spell checker with Google online suggestion system, and using Google online suggestion system alone. We have used precision and recall in order to evaluate the effectiveness of our proposed OCR post-processing. The results show that using Microsoft Office Word with Google outperform other approaches with accuracy of (0.49).