Analysis of deep classification grammar error correction algorithm for online English grammar teaching

The traditional English grammar error correction system has problems such as poor error recognition precision and error correction success rate to be improved. Therefore, in this study, data augmentation techniques were used to transform and process error correcting English texts, and a rule-based and shallow neural network-based English text grammar correction model was constructed. The experimental results showed that the grammar error generation (GEG), rule-based (RB), classification-based (CB), and recurrent neural network (RNN) models achieved accuracy rates of 93.92%, 82.17%, 79.41%, and 88.09% in correcting grammar errors on 1702641 test sentences in the One Billion word corpus, respectively. The experimental results showed that the English grammar error correction model designed in this study had a strong error correction ability, but the computational efficiency was low. The research results significantly improved the accuracy and generalization ability of English grammar correction, optimized learning costs, and brought positive impacts to educational applications, providing strong support for the development of intelligent English grammar correction.

The evaluation of an automatic grammatical error correction system is an important content in the field of automatic grammatical error correction. This paper summarizes the technical routes of the four most representative evaluation metrics of the automatic grammatical error correction systems. Firstly, it introduces the characteristics and composition of each metric, then summarizes its defects, and finally puts forward some suggestions for the future development of the metrics. This paper holds that the application of natural language processing technology should be strengthened in the future development of evaluation metrics.

This paper focuses on the design of an automatic error detection and correction of FSMs for soft errors. Single Event Upsets (SEUs) due to radiation impose an increasing problem to the reliable operation of FPGA used in communication, mile-aero, automotive and industrial designs. By developing safe case sequential logic and fault tolerant state machines with error mitigation circuitry logic, the FPGA design can be protected from SEUs. When a soft error occurs, these method ensures safe design operation by returning the design to a known safe or reset state of operation. This logic provides reliable system operation. The objective of this work is to implement automatic error detection and correction of FSMs for single event upsets is presented. We also analyze how bit errors within same clock cycle can be removed by the addition of error checking bits using SEC-DED code algorithm. The SEC-DED is commonly used as error detection and correction since it is more efficient than parity checker. The SEC-DED code c an be used as the error detection and correction circuitry behind the FSMs to improve the reliable operation. The proposed architecture is simulated in VCS. This structure is synthesized in Xilinx Virtex 7 for implementing on FPGA. The result of the proposed design is analyzed and compared to determine its performance in terms of reliability.

Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation—including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6th edition of the Cell Tracking Challenge.

The application of sequence labeling networks on grammar error detection and correction (GEDC) in English writing primarily on LSTM (Long Short-Term Memory) networks integrated with Conditional Random Fields (CRF). To determine the performance of this method, we consider two publicly distributed databases: the FCE (First Certificate in English) corpus, and the NUCLE (National University of Singapore Corpus of Learner English). The findings indicate greater enhancements of conventional rule-based systems and the previous models of machine learning in identifying and rectifying prevalent maladies in writing. The model proposed presented an F1-Score of 0.89 with the FCE corpus in respect to the subject-verb agreement errors and 0.84 in respect to the NUCLE corpus in the case of tense consistency errors. The model has also demonstrated good results when dealing with prepositional error (F1-Score = 0.75) and article usage error (F1-Score = 0.80). The model attained an overall accuracy of 89%, which is better compared to the rule-based system, which has a tendency of achieving an average accuracy of 70-75 percent.Notwithstanding, this model suffered discourse level errors and word choice errors, having 0.68 F1-Score on word choice mistakes, which means that more advanced problems of grammar were still problematic. The paper indicates the prospect of sequence labeling networks in automating the grammar correction using the results to show deep learning models can be effective in identifying and correcting large sets of grammatical errors. The next step of work will be aimed at the necessity to improve the model to facilitate the treatment of more complex syntactic and discourse-related errors, which will further promote GEDC systems.

The tobacco sorterpsilas high-speed belt conveyor has been choose as an object, the configuration and process of belt error correction have been briefly expounded. Then an automatic error correction system of the belt conveyor has been designed, including mechanical part and control part. One of the emphasis is the choice and installation of sensor and execution unit in this system, the other is the principle, procedure and flow chart of the automatic error correction. By way of practice, the error problem of high-speed belt conveyor has been solved preferably, and the system has a lot of advantages, such as simplicity, low-cost, convenience, steady, and so on.

Automatic detection and correction of errors in the residue number system involves the conversion of residue representations to integers and base extension. The residue number system is generally restricted to moduli that are pairwise relatively prime. In this paper we consider error detection and correction using a moduli set with common factors. A method to construct a moduli set that leads to simplified error detection and correction is presented. Error detection can now be performed by computing residues in parallel. Error correction does not involve base extension any more. It is also shown that, removing all restrictions on the moduli set, leads to more complex error detection/correction algorithms.

Error correction techniques have been used to refine the output sentences from automatic speech recognition (ASR) models and achieve a lower word error rate (WER). Previous works usually adopt end-to-end models and has strong dependency on Pseudo Paired Data and Original Paired Data. But when only pre-training on Pseudo Paired Data, previous models have negative effect on correction. While fine-tuning on Original Paired Data, the source side data must be transcribed by a well-trained ASR model, which takes a lot of time and not universal. In this paper, we propose UCorrect, an unsupervised Detector-Generator-Selector framework for ASR Error Correction. UCorrect has no dependency on the training data mentioned before. The whole procedure is first to detect whether the character is erroneous, then to generate some candidate characters and finally to select the most confident one to replace the error character. Experiments on the public AISHELL-1 dataset and WenetSpeech dataset show the effectiveness of UCorrect for ASR error correction: 1) it achieves significant WER reduction, achieves 6.83% even without fine-tuning and 14.29% after fine-tuning; 2) it outperforms the popular NAR correction models by a large margin with a competitive low latency; and 3) it is an universal method, as it reduces all WERs of the ASR model with different decoding strategies and reduces all WERs of ASR models trained on different scale datasets.

In this paper we present a simple yet effective approach to automatic OCR error detection and correction on a corpus of French clinical reports of variable OCR quality within the domain of foetopathology. While traditional OCR error detection and correction systems rely heavily on external information such as domain-specific lexicons, OCR process information or manually corrected training material, these are not always available given the constraints placed on using medical corpora. We therefore propose a novel method that only needs a representative corpus of acceptable OCR quality in order to train models. Our method uses recurrent neural networks (RNNs) to model sequential information on character level for a given medical text corpus. By inserting noise during the training process we can simultaneously learn the underlying (character-level) language model and as well as learning to detect and eliminate random noise from the textual input. The resulting models are robust to the variability of OCR quality but do not require additional, external information such as lexicons. We compare two different ways of injecting noise into the training process and evaluate our models on a manually corrected data set. We find that the best performing system achieves a 73% accuracy.

Tn this paper, an effective error checking and correction method of or Chinese medical records recognized by OCR is proposed. In our research, an optimized N-gram language model based on vocabulary rather than words is adopted to correct errors, and supervised machine learning based on maximum entropy (MaxEnt) is deployed to build a model for tokenization and named entity recognition. A medical knowledge base (MKB) is established, including dictionaries of medicine, symptoms, diseases, etc., and the frequency of each word as it appeared in the study corpus. Furthermore a Knowledge Base for Error correction (KBE) is built to automatically correct high-frequency errors. With the developed approach, the accuracy rate of the electronic medical record increases from 85.20% to 95.72%, indicating an error reduction of 71.08%.

Inter-basin water transfer projects are core infrastructure for achieving sustainable water resource allocation and addressing regional water scarcity, and pumping station units, as their critical energy-consuming and operation-controlling components, are vital to the projects’ sustainable performance. With the growing complexity and scale of these projects, pumping station units have become more intricate, leading to a gradual rise in failure rates. However, existing fault diagnosis methods are relatively backward, failing to promptly detect potential faults—this not only threatens operational safety but also undermines sustainable development goals: equipment failures cause excessive energy consumption (violating energy efficiency requirements for sustainability), unplanned downtime disrupts stable water supply (impairing reliable water resource access), and even leads to water waste or environmental risks. To address this sustainability-oriented challenge, this paper focuses on the fault characteristics of pumping station units and proposes a comprehensive and accurate fault diagnosis model, aiming to enhance the sustainability of water transfer projects through technical optimization. The model utilizes advanced algorithms and data processing technologies to accurately identify fault types, thereby laying a technical foundation for the low-energy, reliable, and sustainable operation of pumping stations. Firstly, continuous wavelet transform (CWT) converts one-dimensional time-domain signals into two-dimensional time-frequency graphs, visually displaying dynamic signal characteristics to capture early fault features that may cause energy waste. Next, the multi-head attention mechanism (MHA) segments the time-frequency graphs and correlates feature-location information via independent self-attention layers, accurately capturing the temporal correlation of fault evolution—this enables early fault warning to avoid prolonged inefficient operation and energy loss. Finally, the improved convolutional neural network (CNN) layer integrates feature information and temporal correlation, outputting predefined fault probabilities for accurate fault determination. Experimental results show the model effectively solves the difficulty of feature extraction in pumping station fault diagnosis, considers fault evolution timeliness, and significantly improves prediction accuracy and anti-noise performance. Comparative experiments with three existing methods verify its superiority. Critically, this model strengthens sustainability in three key ways: (1) early fault detection reduces unplanned downtime, ensuring stable water supply (a core sustainable water resource goal); (2) accurate fault localization cuts unnecessary maintenance energy consumption, aligning with energy-saving requirements; (3) reduced equipment failure risks minimize water waste and environmental impacts. Thus, it not only provides a new method for pumping station fault diagnosis but also offers technical support for the sustainable operation of water conservancy infrastructure, contributing to global sustainable development goals (SDGs) related to water and energy.

To overcome insufficient feature representation and limited status quantification in active braking systems (ABS), an end-to-end fault diagnosis method of ABS is constructed to combine a one-dimensional convolutional neural network (1D-CNN) and a multi-head attention mechanism (MHAM) under conditions of stronger noise interference and multi-source nonlinear coupling. Based on a 1D-CNN front-end encoder to directly decouple local high-frequency transient features from the original time-series waveform, the long-distance dependency topology within the sequence is deeply reconstructed to quantify the contribution weights of features at different time steps to the evolution of the system's health status. according to the joint constraints of the Adam adaptive optimization algorithm and regularization penalty term, the model effectively avoids the overfitting risk of deep networks and significantly enhances its generalization robustness under unknown and complex conditions. Empirical results strongly demonstrate the superiority of this method. The regression prediction determination coefficient R2 for the degree of failure reaches 0.9472, and the root mean square error (RMSE) is reduced by 1.56%, achieving high-precision quantitative perception of the system's health status.

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.

For second language learners, a reliable and effective Grammatical Error Correction (GEC) system is imperative, since it can be used as an auxiliary assistant for errors correction and helps learners improve their writing ability. Researchers have paid more emphasis on this task with deep learning methods. Better results were achieved on the standard benchmark datasets compared to traditional rule based approaches. We treat GEC as a special translation problem which translates wrong sentences into correct ones like other former works. In this paper, we propose a novel correction system based on sequence to sequence (Seq2Seq) architecture with residual connection and semantically conditioned LSTM (SC-LSTM), incorporating edit operations as special semantic information. Our model further improves the performance of neural machine translation model for GEC and achieves state-of-the-art \(F _{0.5}\)-score on standard test data named CoNLL-2014 compared with other methods that without any re-rank approach.

In this paper, we extend our previous work on deformable image registration to inhomogenous tissues. Inhomogenous tissues include the tissues with embedded tumors, which is common in clinical applications. It is a very challenging task since the registration method that works for homogenous tissues may not work well with inhomogenous tissues. The maximum error normally occurs in the regions with tumors and often exceeds the acceptable error threshold. In this paper, we propose a new error correction method with adaptive weighting to reduce the maximum registration error. Our previous fast deformable registration method is used in the inner loop. We have also proposed a new evaluation metric average error of deformation field (AEDF) to evaluate the registration accuracy in regions between vessels and bifurcation points. We have validated the proposed method using liver MR images from human subjects. AEDF results show that the proposed method can greatly reduce the maximum registration errors when compared with the previous method with no adaptive weighting. The proposed method has the potential to be used in clinical applications to reduce registration errors in regions with tumors.