DPCSpell: A Transformer-based Detector-Purificator-Corrector Framework for Spelling Error Correction of Bangla and Resource Scarce Indic Languages

• URL: http://arxiv.org/abs/2211.03730v3
• Date: Sun, 01 Dec 2024 18:45:39 GMT
• Title: DPCSpell: A Transformer-based Detector-Purificator-Corrector Framework for Spelling Error Correction of Bangla and Resource Scarce Indic Languages
• Authors: Mehedi Hasan Bijoy, Nahid Hossain, Salekul Islam, Swakkhar Shatabda,
• Abstract summary: Spelling error correction is the task of identifying and rectifying misspelled words in texts. Earlier efforts on spelling error correction in Bangla and resource-scarce Indic languages focused on rule-based, statistical, and machine learning-based methods. We propose a novel detector-purificator-corrector, DPC based on denoising transformers by addressing previous issues.
• Score: 2.5874041837241304
• License:
• Abstract: Spelling error correction is the task of identifying and rectifying misspelled words in texts. It is a potential and active research topic in Natural Language Processing because of numerous applications in human language understanding. The phonetically or visually similar yet semantically distinct characters make it an arduous task in any language. Earlier efforts on spelling error correction in Bangla and resource-scarce Indic languages focused on rule-based, statistical, and machine learning-based methods which we found rather inefficient. In particular, machine learning-based approaches, which exhibit superior performance to rule-based and statistical methods, are ineffective as they correct each character regardless of its appropriateness. In this paper, we propose a novel detector-purificator-corrector framework, DPCSpell based on denoising transformers by addressing previous issues. In addition to that, we present a method for large-scale corpus creation from scratch which in turn resolves the resource limitation problem of any left-to-right scripted language. The empirical outcomes demonstrate the effectiveness of our approach, which outperforms previous state-of-the-art methods by attaining an exact match (EM) score of 94.78%, a precision score of 0.9487, a recall score of 0.9478, an f1 score of 0.948, an f0.5 score of 0.9483, and a modified accuracy (MA) score of 95.16% for Bangla spelling error correction. The models and corpus are publicly available at https://tinyurl.com/DPCSpell.

Related papers

• A Coin Has Two Sides: A Novel Detector-Corrector Framework for Chinese Spelling Correction [79.52464132360618]
  Chinese Spelling Correction (CSC) stands as a foundational Natural Language Processing (NLP) task. We introduce a novel approach based on error detector-corrector framework. Our detector is designed to yield two error detection results, each characterized by high precision and recall.
  arXiv  Detail & Related papers  (2024-09-06T09:26:45Z)
• Automatic Real-word Error Correction in Persian Text [0.0]
  This paper introduces a cutting-edge approach for precise and efficient real-word error correction in Persian text. We employ semantic analysis, feature selection, and advanced classifiers to enhance error detection and correction efficacy. Our method achieves an impressive F-measure of 96.6% in the detection phase and an accuracy of 99.1% in the correction phase.
  arXiv  Detail & Related papers  (2024-07-20T07:50:52Z)
• PERCORE: A Deep Learning-Based Framework for Persian Spelling Correction with Phonetic Analysis [0.0]
  This research introduces a state-of-the-art Persian spelling correction system that seamlessly integrates deep learning techniques with phonetic analysis. Our methodology effectively combines deep contextual analysis with phonetic insights, adeptly correcting both non-word and real-word spelling errors. A thorough evaluation on a wide-ranging dataset confirms our system's superior performance compared to existing methods.
  arXiv  Detail & Related papers  (2024-07-20T07:41:04Z)
• Chinese Spelling Correction as Rephrasing Language Model [63.65217759957206]
  We study Chinese Spelling Correction (CSC), which aims to detect and correct the potential spelling errors in a given sentence. Current state-of-the-art methods regard CSC as a sequence tagging task and fine-tune BERT-based models on sentence pairs. We propose Rephrasing Language Model (ReLM), where the model is trained to rephrase the entire sentence by infilling additional slots, instead of character-to-character tagging.
  arXiv  Detail & Related papers  (2023-08-17T06:04:28Z)
• SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings [76.87664008338317]
  Contextual spelling correction models are an alternative to shallow fusion to improve automatic speech recognition. We propose a novel algorithm for candidate retrieval based on misspelled n-gram mappings. Experiments on Spoken Wikipedia show 21.4% word error rate improvement compared to a baseline ASR system.
  arXiv  Detail & Related papers  (2023-06-04T10:00:12Z)
• Persian Typographical Error Type Detection Using Deep Neural Networks on Algorithmically-Generated Misspellings [2.2503811834154104]
  Typographical Error Type Detection in Persian is a relatively understudied area. This paper presents a compelling approach for detecting typographical errors in Persian texts. The outcomes of our final method proved to be highly competitive, achieving an accuracy of 97.62%, precision of 98.83%, recall of 98.61%, and surpassing others in terms of speed.
  arXiv  Detail & Related papers  (2023-05-19T15:05:39Z)
• Correcting diacritics and typos with ByT5 transformer model [0.0]
  People tend to forgo using diacritics and make typographical errors (typos) when typing. In this work, we tackle both problems at once by employing newly-developed ByT5 byte-level transformer models. Our simultaneous diacritics restoration and typos correction approach demonstrates near state-of-the-art performance in 13 languages.
  arXiv  Detail & Related papers  (2022-01-31T13:52:51Z)
• Lexically Aware Semi-Supervised Learning for OCR Post-Correction [90.54336622024299]
  Much of the existing linguistic data in many languages of the world is locked away in non-digitized books and documents. Previous work has demonstrated the utility of neural post-correction methods on recognition of less-well-resourced languages. We present a semi-supervised learning method that makes it possible to utilize raw images to improve performance.
  arXiv  Detail & Related papers  (2021-11-04T04:39:02Z)
• VSEC: Transformer-based Model for Vietnamese Spelling Correction [0.19116784879310028]
  We propose a novel method to correct Vietnamese spelling errors. We tackle the problems of mistyped errors and misspelled errors by using a deep learning model. The experimental results show that our method achieves encouraging performance with 86.8% errors detected and 81.5% errors corrected.
  arXiv  Detail & Related papers  (2021-11-01T00:55:32Z)
• Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction [106.63733511672721]
  We propose a novel language-independent approach to improve the efficiency for Grammatical Error Correction (GEC) by dividing the task into two subtasks: Erroneous Span Detection ( ESD) and Erroneous Span Correction (ESC) ESD identifies grammatically incorrect text spans with an efficient sequence tagging model. ESC leverages a seq2seq model to take the sentence with annotated erroneous spans as input and only outputs the corrected text for these spans. Experiments show our approach performs comparably to conventional seq2seq approaches in both English and Chinese GEC benchmarks with less than 50% time cost for inference.
  arXiv  Detail & Related papers  (2020-10-07T08:29:11Z)
• On the Robustness of Language Encoders against Grammatical Errors [66.05648604987479]
  We collect real grammatical errors from non-native speakers and conduct adversarial attacks to simulate these errors on clean text data. Results confirm that the performance of all tested models is affected but the degree of impact varies.
  arXiv  Detail & Related papers  (2020-05-12T11:01:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.