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A Quantum Language-Inspired Tree Structural Text Representation for Semantic Analysis
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In: Mathematics; Volume 10; Issue 6; Pages: 914 (2022)
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| 4 |
An Unsupervised Approach to Structuring and Analyzing Repetitive Semantic Structures in Free Text of Electronic Medical Records
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In: Journal of Personalized Medicine; Volume 12; Issue 1; Pages: 25 (2022)
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| 5 |
Text Mining from Free Unstructured Text: An Experiment of Time Series Retrieval for Volcano Monitoring
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3503 (2022)
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| 6 |
Detection of Chinese Deceptive Reviews Based on Pre-Trained Language Model
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3338 (2022)
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| 7 |
Automated Customer Complaint Processing for Water Utilities Based on Natural Language Processing—Case Study of a Dutch Water Utility
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In: Water; Volume 14; Issue 4; Pages: 674 (2022)
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| 8 |
MLLP-VRAIN Spanish ASR Systems for the Albayzín-RTVE 2020 Speech-to-Text Challenge: Extension
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In: Applied Sciences; Volume 12; Issue 2; Pages: 804 (2022)
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| 9 |
Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages
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In: Future Internet; Volume 14; Issue 3; Pages: 69 (2022)
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| 10 |
Using Conceptual Recurrence and Consistency Metrics for Topic Segmentation in Debate
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In: Applied Sciences; Volume 12; Issue 6; Pages: 2952 (2022)
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| 11 |
Connecting Text Classification with Image Classification: A New Preprocessing Method for Implicit Sentiment Text Classification
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In: Sensors; Volume 22; Issue 5; Pages: 1899 (2022)
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| 12 |
Predicting Institution Outcomes for Inter Partes Review (IPR) Proceedings at the United States Patent Trial & Appeal Board by Deep Learning of Patent Owner Preliminary Response Briefs
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3656 (2022)
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| 13 |
Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 9; Pages: 5705 (2022)
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| 14 |
FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning
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In: Applied Sciences; Volume 12; Issue 6; Pages: 3130 (2022)
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| 15 |
A Dynamic Attention and Multi-Strategy-Matching Neural Network Based on Bert for Chinese Rice-Related Answer Selection
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In: Agriculture; Volume 12; Issue 2; Pages: 176 (2022)
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| 16 |
Correcting Diacritics and Typos with a ByT5 Transformer Model
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2636 (2022)
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Abstract:
Due to the fast pace of life and online communications and the prevalence of English and the QWERTY keyboard, people tend to forgo using diacritics, make typographical errors (typos) when typing in other languages. Restoring diacritics and correcting spelling is important for proper language use and the disambiguation of texts for both humans and downstream algorithms. However, both of these problems are typically addressed separately: the state-of-the-art diacritics restoration methods do not tolerate other typos, but classical spellcheckers also cannot deal adequately with all the diacritics missing.In this work, we tackle both problems at once by employing the newly-developed universal ByT5 byte-level seq2seq transformer model that requires no language-specific model structures. For a comparison, we perform diacritics restoration on benchmark datasets of 12 languages, with the addition of Lithuanian. The experimental investigation proves that our approach is able to achieve results (>98%) comparable to the previous state-of-the-art, despite being trained less and on fewer data. Our approach is also able to restore diacritics in words not seen during training with >76% accuracy. Our simultaneous diacritics restoration and typos correction approach reaches >94% alpha-word accuracy on the 13 languages. It has no direct competitors and strongly outperforms classical spell-checking or dictionary-based approaches. We also demonstrate all the accuracies to further improve with more training. Taken together, this shows the great real-world application potential of our suggested methods to more data, languages, and error classes.
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Keyword:
ByT5; diacritics restoration; natural language processing; QWERTY; transformer models; typo correction
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URL: https://doi.org/10.3390/app12052636
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| 17 |
The Competitive Advantage of the Indian and Korean Film Industries: An Empirical Analysis Using Natural Language Processing Methods
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4592 (2022)
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| 18 |
eHealth Engagement on Facebook during COVID-19: Simplistic Computational Data Analysis
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 8; Pages: 4615 (2022)
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| 19 |
Cross-Lingual Transfer Learning for Arabic Task-Oriented Dialogue Systems Using Multilingual Transformer Model mT5
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In: Mathematics; Volume 10; Issue 5; Pages: 746 (2022)
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| 20 |
Measuring Gender Bias in Contextualized Embeddings
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In: Computer Sciences & Mathematics Forum; Volume 3; Issue 1; Pages: 3 (2022)
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