Spelling Correction
42 papers with code • 0 benchmarks • 4 datasets
Spelling correction is the task of detecting and correcting spelling mistakes.
Benchmarks
These leaderboards are used to track progress in Spelling Correction
GitHub Typo Corpus
CSCD-IME
MCSCSet
Viwiki-Spelling
Latest papers with no code
Large Language Models for Simultaneous Named Entity Extraction and Spelling Correction
Language Models (LMs) such as BERT, have been shown to perform well on the task of identifying Named Entities (NE) in text.
Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework
Chinese Spelling Correction (CSC) aims to detect and correct spelling errors in given sentences.
Multi-teacher Distillation for Multilingual Spelling Correction
Accurate spelling correction is a critical step in modern search interfaces, especially in an era of mobile devices and speech-to-text interfaces.
Sampling and Ranking for Digital Ink Generation on a tight computational budget
We use and compare the effect of multiple sampling and ranking techniques, in the first ablation study of its kind in the digital ink domain.
Persian Typographical Error Type Detection Using Deep Neural Networks on Algorithmically-Generated Misspellings
Therefore, this paper presents a compelling approach for detecting typographical errors in Persian texts.
Beqi: Revitalize the Senegalese Wolof Language with a Robust Spelling Corrector
In this paper, we present a way to address the constraint related to the lack of data by generating synthetic data and we present sequence-to-sequence models using Deep Learning for spelling correction in Wolof.
Cleansing Jewel: A Neural Spelling Correction Model Built On Google OCR-ed Tibetan Manuscripts
Then, we implemented a Confidence Score mechanism into the Transformer architecture to perform spelling correction tasks.
Improving Contextual Spelling Correction by External Acoustics Attention and Semantic Aware Data Augmentation
To solve above limitations, in this paper we propose an improved non-autoregressive (NAR) spelling correction model for contextual biasing in E2E neural transducer-based ASR systems to improve the previous CSC model from two perspectives: Firstly, we incorporate acoustics information with an external attention as well as text hypotheses into CSC to better distinguish target phrase from dissimilar or irrelevant phrases.
Correcting Real-Word Spelling Errors: A New Hybrid Approach
The real-word correction model proposed by Mays, Damerau and Mercer showed a great performance in different evaluations.
Real-Word Error Correction with Trigrams: Correcting Multiple Errors in a Sentence
Spelling correction is a fundamental task in Text Mining.
