Erroneous Words Research Articles

The Impact of COVID-19 on ‘Spanish-Speaking’ Children’s Phonological Development

Communication and social interaction have been limited during the COVID-19 pandemic. The aim of this study was to check if 3-, 4-, and 5-year-old children manifest alterations in oral language according to their stage of language development. To carry it out, the Induced Phonological Register developed by Monfort and Juárez was applied to analyze 150 participants (77 boys and 73 girls) with an average age of 4 years and 6 months. Children who experienced the COVID-19 pandemic for an extended period show a delay in the acquisition of some phonemes compared to children who undergo typical phonological development as specified by Laura Bosch (2003) regarding language development for Spanish children. Likewise, they present a higher number of erroneous words and phonemes than expected for their age.

Code‐mixed Hindi‐English text correction using fuzzy graph and word embedding

AbstractInteraction via social media involves frequent code‐mixed text, spelling errors and noisy elements, which creates a bottleneck in the performance of natural language processing applications. This proposed work is the first approach for code‐mixed Hindi‐English social media text that comprises language identification, detection and correction of non‐word (Out of Vocabulary) errors as well as real‐word errors occurring simultaneously. Each identified language (Devanagari Hindi, Roman Hindi, and English) has its own complexities and challenges. Errors are detected individually for each language and a suggestive list of the erroneous words is created. After this, a fuzzy graph between different words of the suggestive lists is generated using various semantic relations in Hindi WordNet. Word embeddings and Fuzzy graph‐based centrality measures are used to find the correct word. Several experiments are performed on different social media datasets taken from Instagram, Twitter, YouTube comments, Blogs, and WhatsApp. The experimental results demonstrate that the proposed system corrects out‐of‐vocabulary words as well as real‐word errors with a maximum recall of 0.90 and 0.67, respectively for Dev_Hindi and 0.87 and 0.66, respectively for Rom_Hindi. The proposed method is also applied for state‐of‐art sentiment analysis approaches where the F1‐score has been visibly improved.

Morphology-Based Spell Checker for Dawurootsuwa Language

Processing of textual information by using word-processing tools is extremely increased due to the presence of misspelled or erroneous words. In order to minimize these misspelled words from digital information, different spellchecker tools are needed. A plenty of works are performed in technological favored languages like English and European languages but not for an underresourced language like Dawurootsuwa. The primary idea behind a morphology-based spellchecker is to use a dictionary lookup approach with morphological properties of the language to reduce dictionary size while also handling word inflection, derivation, and compounding. Two distinct tests were carried out in this work to evaluate the performance of a morphology-based spellchecker: error detection and error correction. The Hunspell dictionary format was utilized to construct the root words in this study, which included a total of 5,000 root words and more than 2,500 morphological rules along with 3,156 unique words for testing. The experimental result showed the overall spell error detection performance of 90.4% and the overall spell error correction performance of 79.31%. Moreover, we are working further towards developing a real word spelling checker that incorporate more numbers of language rules.

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.

The Interlanguage Speech Intelligibility Benefit for Turkish Speakers of English

This study is aimed at investigating the Interlanguage Speech Intelligibility Benefit (ISIB) via native English and Turkish listeners’ transcriptions of Turkish talkers’ words with pronunciation errors in English. Speech samples collected from talkers with a Turkish L1 background (N=16) were presented to 33 L1 English and 33 L1 Turkish listeners via an instrument to be filled with the transcriptions of erroneous target words. Results supported matched ISIB for listeners as Turkish talkers were more intelligible to Turkish listeners in a considerably higher number of target words. Similarly, a higher number of target words were more intelligible to Turkish listeners with statistically significant differences. Most of these words had been labelled erroneous due to L1 transfer caused by orthographic interference, mispronunciations of English sounds that do not exist in Turkish, and loanwords found in similar forms in Turkish. Turkish listeners appeared to rely on such cues in Turkish talkers’ speech.

О ТОЧНОСТИ И ТРУДОЕМКОСТИ МНОГОЭТАПНОГО МЕТОДА КОРРЕКЦИИ ИСКАЖЕННЫХ ТЕКСТОВ В ЗАВИСИМОСТИ ОТ СТЕПЕНИ ИСКАЖЕНИЯ

One of the main factors that significantly complicate the understanding, translation andanalysis of texts obtained by automatic recognition of speech or images of texts is the presence ofdistortions in the form of erroneous symbols, words and phrases. Until recently, there were noeffective software tools for correcting texts with significant distortions, although this task is relevantboth for Russian and other common languages in the context of the active use of recognitionsystems in advanced augmented reality systems. The authors proposed a new multi-stage methodfor correcting distorted texts, which significantly increases the accuracy of the correction (interms of the number of correctly corrected words in the text) and is based on the sequential detectionof errors and their correction. In this paper, we evaluate the accuracy and computationalcomplexity of the proposed method for correcting distorted texts at various levels of distortion, anddetermine its place among other modern approaches to correction. The most typical errors ofrecognition systems are: – replacing a word with a similar sound or graphic spelling; – replacingseveral words with one; – replacing one word with several; – omission of words; – insertion ordeletion of short words (including prepositions and conjunctions). As a result of recognition, adistorted text is obtained, which consists mainly of dictionary words, even in places of distortion.With a large number of distortions, the texts become almost unreadable. Due to the fact that it isproblematic to select texts with a wide range of distortion levels in the required amount based onthe results of real machine recognition of speech and images of texts, software modeling of distortionswas used. A text distortion technique has been proposed and implemented that simulates theresults of recognition systems in a wide range of distortions; distorted texts have been prepared inthe required amount. Within the framework of the proposed multi-stage correction method, nondictionaryword forms and words are considered distorted if the probability of their occurrence inthe text in accordance with the chosen language model is less than a given threshold. For suchdistorted words, a list of possible variants of words is built, which includes only those word formsfrom the dictionary that are at a certain Levenshtein distance from the word under study. The correctedtext from the tables of word variants is obtained by searching for the most probable chainof word forms. The correction method consists of several stages, at each stage only those fragmentsof the text that remain distorted after the previous stage are corrected. According to theresults of the experiments on the correction of distorted texts, it was concluded that the proposedcorrection method showed good results with an average value of F-measure >50 % in the distortionrange from 0 to 75 %. Linguistic experts confirmed the fruitfulness of the proposed approachto correction and its preference over other modern approaches, fixing that with a level of distortionof up to 50 % of words, the corrected text is read with much less effort than a distorted one,and with a level of distortion of up to 70% of words, the corrected text also allows you to highlightuseful information about the content.

Efficient Weighted Edit Distance and N-gram Language Models to Improve Spelling Correction of Segmentation Errors

In most research that has dealt with the correction of spelling errors, the errors are caused by the misuse of space (deletion or insertion of space) are not tackled. Forgetting to deal with this type of errors in the texts poses a problem of understanding and ambiguity of the meaning of the sentence containing these errors. In this article, we propose a new approach to correct errors due to the insertion of space in a word, and at the same time correct other types of editing errors. This approach is based on the edit distance and uses bi-grams language models to correct words in context. The test conducted on hundreds of erroneous words (by insertion of space and/or by simple editing errors) made it possible to assess the relevance and validity of the methods developed to correct this type of error. The approaches proposed in this article provide a very important clarification and reminder by comparing them to those of other existing approaches.

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.

Grammar checking and relation extraction in text: approaches, techniques and open challenges

Purpose The purpose of this paper is to provide an overall review of grammar checking and relation extraction (RE) literature, their techniques and the open challenges associated with them; and, finally, suggest future directions. Design/methodology/approach The review on grammar checking and RE was carried out using the following protocol: we prepared research questions, planed for searching strategy, addressed paper selection criteria to distinguish relevant works, extracted data from these works, and finally, analyzed and synthesized the data. Findings The output of error detection models could be used for creating a profile of a certain writer. Such profiles can be used for author identification, native language identification or even the level of education, to name a few. The automatic extraction of relations could be used to build or complete electronic lexical thesauri and knowledge bases. Originality/value Grammar checking is the process of detecting and sometimes correcting erroneous words in the text, while RE is the process of detecting and categorizing predefined relationships between entities or words that were identified in the text. The authors found that the most obvious challenge is the lack of data sets, especially for low-resource languages. Also, the lack of unified evaluation methods hinders the ability to compare results.

An Erroneous Word Locating Algorithm for Natural Language Text in Medical Scene Based on Clustering Analysis

With the development of technology, the processing of natural language texts is increasingly used in medical scenes. For example, speech recognition technology and smart customer service robots can help hospitals to improve efficiency. However, the accuracy rate of the text output of the speech recognition is not satisfactory, especially in the medical scene. As a result, the study of natural language text processing in medical scene is very meaningful. To address this issue, we construct a corpus, a knowledge base, a Pinyin library and a word frequency library. We perform word segmentation operations based on the knowledge base and calculate the word co-occurrence probability based on the word frequency library. The word co-occurrence probability is used to determine whether erroneous words exist or not. If there are words whose co-occurrence probabilities are below a threshold, we assert that there exists erroneous words. And then the clustering analysis is used to locate the erroneous words. The experimental results show that the accuracy rate of erroneous word locating is very high. Therefore, this method has certain practical value.

Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings

In this study, we improve grammatical error detection by learning word embeddings that consider grammaticality and error patterns. Most existing algorithms for learning word embeddings usually model only the syntactic context of words so that classifiers treat erroneous and correct words as similar inputs. We address the problem of contextual information by considering learner errors. Specifically, we propose two models: one model that employs grammatical error patterns and another model that considers grammaticality of the target word. We determine grammaticality of n-gram sequence from the annotated error tags and extract grammatical error patterns for word embeddings from large-scale learner corpora. Experimental results show that a bidirectional long-short term memory model initialized by our word embeddings achieved the state-of-the-art accuracy by a large margin in an English grammatical error detection task on the First Certificate in English dataset.

The Effects of Written Feedback On ESL Writers’ Ability To Edit Word Choice Errors

This study examines whether indirect written corrective feedback (CF) can enable 45 ESL writers with intermediate language proficiency to self-edit word choice errors classified as conceptual. Using a pre- and immediate post-test design, the study compares the effects of indirect CF under two conditions: errors are marked and coded without (1) and with metalinguistic explanation and (2) with two types of metalinguistic explanation: traditional and cognitive. Accuracy of word choice is measured in a new piece of writing. The results indicate that (1) CF with metalinguistic explanation is more useful than that without explanation (the control group) and (2) cognitive explanation (the cognitive group) appears to be significantly more effective than one drawn on the traditional account of language (the traditional group). The findings suggest that, when the CF attends to word choice errors as conceptual by addressing the mismatch between L2 forms and their conceptual content structured through cognitive frames and conceptual metaphors, ESL student writers are likely to make correct assumptions about syntagmatic connections of L2 words and correct more erroneous words in their L2 writing than when they are exposed to the feedback that approaches word choice errors as simply lexical and focuses on form-form mismatches.

The vocabulary and the morphology in spell checker

Automatic spell checker systems aim to verify and correct erroneous words through a suggested set of words that are the nearest lexically to the erroneous ones. The performance of such corrective systems differs according to the correction mechanisms implemented. The lexis of the automatic spell checker tools represents a main disadvantage. In this paper, we address the lexicon insufficient problem used in the automatic spell checker and we treated the problem of vocabulary management and its relation to the performance of a system of automatic spelling correction. In the first place, we developed an approach that aims to correct the derived words, considering that the most Arabic words are derived ones by adjusting the Levenshtein algorithm to our need, then, we show a dictionary management that helps us to minimize the number of comparisons.In this context, two axis were studied, the organizational aspect and the morphological aspect. For the first axis, we proposed a vocabulary management allowing the optimization of the access number to the lexicon’s file. For the morphological level, we exploited the derivation notion to reduce the number of the words to compare avoiding redundant comparisons.

Phonological characteristics and rehabilitation training of abnormal velar in children with functional articulation disorders

To explore the phonological characteristics and rehabilitation training of abnormal velar in patients with functional articulation disorders (FAD). Eighty-seven patients with FAD were observed of the phonological characteristics of velar. Seventy-two patients with abnormal velar accepted speech training. The correlation and simple linear regression analysis were carried out on abnormal velar articulation and age. The articulation disorder of /g/ mainly showed replacement by /d/, /b/ or omission. /k/ mainly showed replacement by /d/, /t/, /g/, /p/, /b/. /h/ mainly showed replacement by /g/, /f/, /p/, /b/ or omission. The common erroneous articulation forms of /g/, /k/, /h/ were fronting of tongue and replacement by bilabial consonants. When velar combined with vowels contained /a/ and /e/, the main error was fronting of tongue. When velar combined with vowels contained /u/, the errors trended to be replacement by bilabial consonants. After 3 to 10 times of speech training, the number of erroneous words decreased to (6.24±2.61) from (40.28±6.08) before the speech training was established, the difference was statistically significant (Z=-7.379, P=0.000). The number of erroneous words was negatively correlated with age (r=-0.691, P=0.000). The result of simple linear regression analysis showed that the determination coefficient was 0.472. The articulation disorder of velar mainly shows replacement, varies with the vowels. The targeted rehabilitation training hereby established is significantly effective. Age plays an important role in the outcome of velar.

The Context in Automatic Spell Correction

Abstract Automatic spell checker systems aim to verify and correct erroneous words through a suggested set of words that are the nearest lexically to the erroneous ones. However, the main disadvantage of those systems is that the wanted solution may bethe most distant lexically but the most appropriate in context. To correctly sort these solutions, the corrector must have some information about the general context of the text; the paragraph or the neighboring words of the misspelled word. In this paper, we will present an automatic spell checking method according to the context. This method is based on a learning corpus containing a distribution of the appearance probability of a word in different contexts. It combines the lexical correction via the Levenstein algorithm and the context based correction through a well definedlearning corpus which is composed of documents collected from the internet. This approach has proven efficiency and the obtained results are much more improved and satisfactory.

English as a lingua franca in Europe: bilingualism and multicompetence

Many multilinguals in Europe habitually use the linguistic strategies often attributed to users of English as a lingua franca (ELF). ELF, in which native speaker norms are not invoked, may be the perfect arena for multilinguals to exploit what Vivian Cook calls ‘multicompetence’, a dynamic multilingual system in which more than one language can be activated at the same time. What might be considered, in the use of a native language, and from an SLA perspective, as accidental transfer lapses, erroneous words, deviant idioms and collocations and unconscious calques, can equally be seen as signs of linguistic awareness and enhanced communicative competence when used in ELF. Many Europeans can understand a large amount of cognate lexis from different languages, making borrowing and code-switching viable strategies even where interlocutors do not share the same languages, especially when speaking English, a Germanic language with a huge amount of Latinate and French lexis. This article considers the potential and the limitations of language mixing by speakers of ELF. Allan James has suggested that European ELF may be a kind of performance without competence, but it might equally be seen as a widespread instantiation of multicompetence in action.

Towards Automatic Error Analysis of Machine Translation Output

Evaluation and error analysis of machine translation output are important but difficult tasks. In this article, we propose a framework for automatic error analysis and classification based on the identification of actual erroneous words using the algorithms for computation of Word Error Rate (WER) and Position-independent word Error Rate (PER), which is just a very first step towards development of automatic evaluation measures that provide more specific information of certain translation problems. The proposed approach enables the use of various types of linguistic knowledge in order to classify translation errors in many different ways. This work focuses on one possible set-up, namely, on five error categories: inflectional errors, errors due to wrong word order, missing words, extra words, and incorrect lexical choices. For each of the categories, we analyze the contribution of various POS classes. We compared the results of automatic error analysis with the results of human error analysis in order to investigate two possible applications: estimating the contribution of each error type in a given translation output in order to identify the main sources of errors for a given translation system, and comparing different translation outputs using the introduced error categories in order to obtain more information about advantages and disadvantages of different systems and possibilites for improvements, as well as about advantages and disadvantages of applied methods for improvements. We used Arabic–English Newswire and Broadcast News and Chinese–English Newswire outputs created in the framework of the GALE project, several Spanish and English European Parliament outputs generated during the TC-Star project, and three German–English outputs generated in the framework of the fourth Machine Translation Workshop. We show that our results correlate very well with the results of a human error analysis, and that all our metrics except the extra words reflect well the differences between different versions of the same translation system as well as the differences between different translation systems.

Visually and Phonologically Similar Characters in Incorrect Chinese Words

Information about students’ mistakes opens a window to an understanding of their learning processes, and helps us design effective course work to help students avoid replication of the same errors. Learning from mistakes is important not just in human learning activities; it is also a crucial ingredient in techniques for the developments of student models. In this article, we report findings of our study on 4,100 erroneous Chinese words. Seventy-six percent of these errors were related to the phonological similarity between the correct and the incorrect characters, 46% were due to visual similarity, and 29% involved both factors. We propose a computing algorithm that aims at replication of incorrect Chinese words. The algorithm extends the principles of decomposing Chinese characters with the Cangjie codes to judge the visual similarity between Chinese characters. The algorithm also employs empirical rules to determine the degree of similarity between Chinese phonemes. To show its effectiveness, we ran the algorithm to select and rank a list of about 100 candidate characters, from more than 5,100 characters, for the incorrectly written character in each of the 4,100 errors. We inspected whether the incorrect character was indeed included in the candidate list and analyzed whether the incorrect character was ranked at the top of the candidate list. Experimental results show that our algorithm captured 97% of incorrect characters for the 4,100 errors, when the average length of the candidate lists was 104. Further analyses showed that the incorrect characters ranked among the top 10 candidates in 89% of the phonologically similar errors and in 80% of the visually similar errors.

“This sentence is wrong.” Detecting errors in machine-translated sentences

Machine translation systems are not reliable enough to be used as is: except for the most simple tasks, they can only be used to grasp the general meaning of a text or assist human translators. The purpose of confidence measures is to detect erroneous words or sentences produced by a machine translation system. In this article, after reviewing the mathematical foundations of confidence estimation, we propose a comparison of several state-of-the-art confidence measures, predictive parameters and classifiers. We also propose two original confidence measures based on Mutual Information and a method for automatically generating data for training and testing classifiers. We applied these techniques to data from the WMT campaign 2008 and found that the best confidence measures yielded an Equal Error Rate of 36.3% at word level and 34.2% at sentence level, but combining different measures reduced these rates to 35.0% and 29.0%, respectively. We also present the results of an experiment aimed at determining how helpful confidence measures are in a post-editing task. Preliminary results suggest that our system is not yet ready to efficiently help post-editors, but we now have both software and a protocol that we can apply to further experiments, and user feedback has indicated aspects which must be improved in order to increase the level of helpfulness of confidence measures.