Spell Checking System Research Articles

Technologies Overview for Typo Segregation

The article focuses particularly on the difference between typos (accidental mechanical errors) and spelling or conceptual errors that arise from insufficient knowledge of language rules. Modern typo detection methods are analyzed, highlighting the advantages and disadvantages of each. The Levenshtein method is one of the most common algorithms for detecting and correcting errors in text. It effectively identifies and corrects errors in short words where the number of operations to convert the erroneous word to the correct one is small. However, this method does not consider the context in which the word is used, which can lead to incorrect corrections. The keyboard layout-based typo detection method analyzes probable errors that can occur due to the proximity of keys on the keyboard. It is simple to implement and integrate into existing spell-checking systems but does not consider the context of word usage. The contextual analysis method for typo detection relies on using contextual information to identify and correct errors in text, requiring significant computational resources and a large, diverse corpus of texts for effective model training. Deep models, such as BERT or GPT, consider the context of entire sentences or even larger text blocks, allowing for high accuracy in typo detection but require significant computational resources for training and inference, as well as large volumes of high-quality data for training. Machine learning methods, such as n-grams and Bayesian classifiers, show significant potential due to their simplicity and efficiency but may not account for complex dependencies between words and context, reducing their accuracy. The study highlights the importance of accurate error detection in student assessment systems, where typos can affect final grades and the relevance of answers.

Automatic Spell-Checking System for Spanish Based on the Ar2p Neural Network Model

Currently, approaches to correcting misspelled words have problems when the words are complex or massive. This is even more serious in the case of Spanish, where there are very few studies in this regard. So, proposing new approaches to word recognition and correction remains a research topic of interest. In particular, an interesting approach is to computationally simulate the brain process for recognizing misspelled words and their automatic correction. Thus, this article presents an automatic recognition and correction system of misspelled words in Spanish texts, for the detection of misspelled words, and their automatic amendments, based on the systematic theory of pattern recognition of the mind (PRTM). The main innovation of the research is the use of the PRTM theory in this context. Particularly, a corrective system of misspelled words in Spanish based on this theory, called Ar2p-Text, was designed and built. Ar2p-Text carries out a recursive process of analysis of words by a disaggregation/integration mechanism, using specialized hierarchical recognition modules that define formal strategies to determine if a word is well or poorly written. A comparative evaluation shows that the precision and coverage of our Ar2p-Text model are competitive with other spell-checkers. In the experiments, the system achieves better performance than the three other systems. In general, Ar2p-Text obtains an F-measure of 83%, above the 73% achieved by the other spell-checkers. Our hierarchical approach reuses a lot of information, allowing for the improvement of the text analysis processes in both quality and efficiency. Preliminary results show that the above will allow for future developments of technologies for the correction of words inspired by this hierarchical approach.

SentiVerb system: classification of social media text using sentiment analysis

This article proposes novel frameworks of SentiVerb and Spell Checker system, which extracts the reaction, mood, and opinion of users from social media text (SMT). The opinion of users is extracted from their written text on social media such as comments, tweets, blogs, feedbacks etc. and are classified as positive or negative opinion based on sentiment score of SMT using dictionary-based approach and a binary classifier. The dictionary-based approach uses opinion verb dictionary (OVD) to extract the sentiment of opinion verbs present in SMT. This OVD contain only opinion verbs along with their sentiment score. The various steps of the framework such as lower-case conversion, tokenization, spell checker, Part-of-Speech tagging, stop word elimination, stemming, sentiment score calculation, and classification of SMT has been discussed. A new concept of threshold negative parameter is first time introduced in this article. In the experiment, the proposed SentiVerb system’s performance is evaluated on three datasets such as Facebook comments on goods and services tax (GST) implementation in India, tweets on the debate between former president of USA Mr. Barack Obama and Mr. John McCain, and the movie reviews. Consequently, the implementation of the proposed SentiVerb system using rule-based classifier (RBC) gives the best performance result in term of accuracy with 82.5% on GST comments and 79.18% on Obama-McCain debate, which is better than the existing algorithms on the social issues related domain dataset(s). Also, system performance (accuracy of 71.3%) is better than others results on standard movie dataset.

Arabic Part-of-Speech Tagger, an Approach Based on Neural Network Modelling

POS-tagging gained the interest of researchers in computational linguistics sciences in the recent years. Part-of-speech tagging systems assign the proper grammatical tag or morpho-syntactical category labels automatically to every word in the corpus per its appearance on the text. POS-tagging serves as a fundamental and preliminary step in linguistic analysis which can help in developing many natural language processing applications such as: word processing systems, spell checking systems, building dictionaries and in parsing systems. Arabic language gained the interest of researchers which led to increasing demand for Arabic natural language processing systems. Artificial neural networks has been applied in many applications such as speech recognition and part of speech prediction, but it is considered as a new approach in Part-of-speech tagging. In this research, we developed an Arabic POS-tagger using artificial neural network. A corpus of 20,620 words, which were manually assigned to the appropriate tags was developed and used to train the artificial neural network and to test the part of speech tagger systems’ overall performance. The accuracy of the developed tagger reaches 89.04% using the testing dataset. While, it reaches 98.94% using the training dataset. By combining the two datasets, the accuracy rate for the whole system is 96.96%.

Context's impact on the automatic spelling correction

This paper aims to shed light on a mechanism that will be used to exploit topical context information improving the accuracy of the automatic spell checking system. This study aims at solving the problem encountered by the auto correct spell checking system, which resides in the fact that the requested solution might be located in the last position. We have implemented a set of techniques in order to build a context oriented spelling corrector and by the end of this work the designed corrector will essentially be using a dictionary that contains a distribution of probability of a word occurrence in various contexts. This latter is constructed by bringing a collection of documents available via the internet.

A Combined Spell Checking and Error Correcting System for Punjabi -Hindi Language using Hybrid Approach

Spellchecker is software that analyzes possible misspellings in the text. It is the process of detecting and sometimes providing some suggestions for incorrectly spelled words in a text. If dictionary of spell checker is larger than higher is the error detection and error correction rate. Though considerable work has been done in English language but not much work has been done in regional languages of India including Punjabi and Hindi. Punjabi is the official language of Punjab state in India Punjabi is world’s 12th most widely spoken language In Punjabi language, there is very small amount of work is completed in this region.. Hindi is the official language of 11 vowels and 33 consonants. Hindi is also the third most spoken language in the world. A few work is done in Punjabi-Hindi spell detection and correction field and it is not easy task to identify errors in Punjabi-Hindi text. The spell checker systems are online available but as not stand-alone applications. The only available spell checker for Punjabi is “Akhar” ”Raftaar” and “Sudhaar”. “Akhar” is paid software that is not available free for its use to everybody and “Sudhaar” spell checker is a desktop application Some paid Hindi spell checker software’s are also online available. “Hinspell” & “Hinkhoj” are available spell checkers for Hindi language but a lot of improvement is needed. NLP (Natural language processing) is a field of computer science concerned with interaction between computer and human language. We have developed a combined spell checker and error correcting system for both Punjabi and Hindi Language. We used hybrid approach to implement the Spelling checking and Correcting System. The proposed system use hybrid approach which s a combination of various approaches like rule based approach, dictionary lookup approach , edit distance approach and NGram approach. Proposed system is tested with various inputs collected from different sources and results are found very accurate than that of existing system. Keywords: Spell Checking, Error Detection, Error Correction, Punjabi, Hindi, Dictionary-lookup, Hybrid approach.

Phonetic-based Sindhi spellchecker system using a hybrid model

This article presents a novel architecture using a hybrid model for developing a Sindhi spellchecker system which has yet not been developed prior to this work. The compound textual forms and glyphs of Sindhi language presents a substantial challenge for developing a Sindhi spellchecker system and generating a similar suggestion list for misspelled words. In order to implement such a system, phonetic-based Sindhi language rules and patterns must be taken into account for increasing the accuracy and efficiency. In this research work, a simple and efficient combinational hybrid system is proposed, using three different algorithms, the Edit Distance algorithm to find the measure of similarity between two Sindhi strings. The phonetic-based SoundEx and ShapeEx algorithms are developed for pattern or glyph matching, generating accurate and an efficient suggestion list for incorrect or misspelled Sindhi words. The proposed system is established with a blend between Phonetic-based SoundEx algorithm and ShapeEx algorithm for pattern or glyph matching, generating accurate and efficient suggestion list for incorrect or misspelled Sindhi words. In this article, a table of phonetically similar-sounding Sindhi characters is presented which are grouped together along with another table containing similar glyph or shape-based character groups. The system has been successfully integrated into a pre-developed Sindhi word processer application. The Sindhi word segmentation methodology and algorithms required for the spellchecker has already been published and so are not discussed in detail in this article.

Intelligent typo correction for text mining through machine learning

Typo detection and correction is an important process in many text mining applications. This research focuses on automatic typo detection and correction for processing text documents that are unstructured, contain many grammar and spelling errors, and have many self-invented terminologies that can be interpreted only through domain-specific knowledge. In this paper we present an intelligent typo detection and correction ITDC system. Its 'intelligence' is reflected by automatically identifying and accurately correcting a broad range of typos, from simple typos such as duplication, omission, transposition, substitution characters, to complex spelling errors, such as word boundary errors, unconventional use of acronyms, etc. ITDC utilises general language knowledge and domain-specific knowledge extracted by machine learning algorithms. It is evaluated through a case study that involves the automatic processing of automotive fault diagnostic text documents. The experiment results show that the proposed system outperforms some of the state-of-art spell checking systems.

Novel Approach for Arabic Spell-Checker: Based on Radix Search Tree

The main aim of this study is to develop a spell-checker system for Arabic language. This is done by investigating the viability of applying the radix search tree approach. Through this scientific research several shrubs that represent Arabic characters will be built through serialized tracking of characters word where it can be added to the dictionary and with a special mark in the node that contains the last characters from each word; on other side during searching process, every word can be tracked character by character according suitable path inside its shrub, Accordingly, correct word can be recognized if and only if searching process locates some leaves during the traverse of the shrub. Otherwise, the word will be considered incorrect. General Terms Your general terms must be any term which can be used for general classification of the submitted material such as Pattern Recognition, Security, Algorithms et. al.

For an Independent Spell-Checking System from the Arabic Language Vocabulary

In this paper, we propose a new approach for spell-checking errors committed in Arabic language. This approach is almost independent of the used dictionary, of the fact that we introduced the concept of morphological analysis in the process of spell-checking. Hence, our new system uses a stems dictionary of reduced size rather than exploiting a large dictionary not covering the all Arabic words. The obtained results are highly positive and satisfactory; this has allowed us to appreciate the validity of our concept and shows the importance of our new approach.

FarsiSpell: A spell-checking system for Persian using a large monolingual corpus

In recent years, great availability of various language resources in different forms as well as rapid development of computer technology and programming skills have made researchers in the fields of linguistics and computer science cooperate in solving different problems of computational linguistics and natural language processing. Building large monolingual as well as bilingual corpora in digital forms and storing them in computer memories has enabled linguists and lan- guage engineers to automatically explore techniques for processing information with the help of various computer programs without any need to manually col- lect and analyze data. One of the main applications of monolingual corpora can be seen in developing automatic spell-checking systems. In such systems, a large monolingual corpus can function as a database instead of a monolingual dictionary. In the present study, it has been tried to demonstrate the effectiveness of a large monolingual corpus of Persian in improving the output quality of a spell-checker developed for this language. In the present spelling correction system, the three phases of error detection, making suggestions, and ranking suggestions are performed in three separate stages. An experiment was carried out to evaluate the performance of the spell-checking system.

Effective spell checking by learning user behavior

This paper describes a spell checking system that learns user behavior. Based on that insight, the system with high likelihood suggests correct replacements for incorrect words and declares unknown, but correct words to be correct. The system relies on three dictionaries, a so-called user history file, and two logic modules to carry out the learning and spell checking. Tests have proved that the system is very fast and highly reliable. Specifically, the top ranked replacement word for an incorrect word was the correct word 96% of the time. Words that were not in the large dictionary but that nevertheless were correct, for example, persons' names, compound words, and control commands, were declared to be correct 82% of the time. It was never observed that an incorrect word was accepted as correct.