Statistical Context-Sensitive Spelling Correction Using Typing Error Rate

Kim, Minho; Jin, Jingzhi; Kwon, Hyuk‐Chul; Yoon, Ae-Sun

doi:10.1109/cse.2013.185

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Large scales of query logs and related corpus are also necessary for their model training. Many machine learning and natural language processing technologies which are very effective in textual entailment, free translation and transliteration, and sentence analysis have been introduced into query checking task [6]. In addition, some correction features are learned from these query logs [7,8].…”

Section: Related Workmentioning

confidence: 99%

Error Checking for Chinese Query by Mining Web Log

Duan

Liu

2015

Mathematical Problems in Engineering

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For the search engine, error-input query is a common phenomenon. This paper uses web log as the training set for the query error checking. Through then-gram language model that is trained by web log, the queries are analyzed and checked. Some features including query words and their number are introduced into the model. At the same time data smoothing algorithm is used to solve data sparseness problem. It will improve the overall accuracy of then-gram model. The experimental results show that it is effective.

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Section: Related Workmentioning

confidence: 99%

Error Checking for Chinese Query by Mining Web Log

Duan

Liu

2015

Mathematical Problems in Engineering

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“…While effective in preserving sentence structure, this approach has its pitfalls: it is labor-intensive and rigid, failing to catch errors outside predefined rules. To mitigate this, statistical approaches have been introduced, basing error judgments on corpus-derived probabilities [5,6]. However, these require substantial corpora to achieve satisfactory performance.…”

Section: Introductionmentioning

confidence: 99%

Towards Harnessing the Most of ChatGPT for Korean Grammatical Error Correction

Park,

Koo,

Kim

et al. 2024

Applied Sciences

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In this study, we conduct a pioneering and comprehensive examination of ChatGPT’s (GPT-3.5 Turbo) capabilities within the realm of Korean Grammatical Error Correction (K-GEC). Given the Korean language’s agglutinative nature and its rich linguistic intricacies, the task of accurately correcting errors while preserving Korean-specific sentiments is notably challenging. Utilizing a systematic categorization of Korean grammatical errors, we delve into a meticulous, case-specific analysis to identify the strengths and limitations of a ChatGPT-based correction system. We also critically assess influential parameters like temperature and specific error criteria, illuminating potential strategies to enhance ChatGPT’s efficacy in K-GEC tasks. Our findings offer valuable contributions to the expanding domain of NLP research centered on the Korean language.

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“…Contrastingly, real-word errors give rise to an existing word that is orthographically correct, but incorrect in that particular context ( dessert for desert , or their for there ); for that reason, they are also called context-dependent errors. These errors affect the syntactic and semantic level, and their causes can be cognitive, typographic or phonetic, among others (Kim et al, 2013). The concepts of context-dependent errors and real-word errors will be used interchangeably throughout this work.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Context-Dependent Errors in the Medical Domain in Spanish: A Corpus-Based Study

Hernández

Molina²,

Almela

2023

SAGE Open

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This corpus-based study aimed to investigate the presence of context-dependent linguistic errors in a corpus of clinical reports. The data were taken from a corpus comprising more than 2 million words and made up of clinical reports from emergency medicine, intensive care unit, general surgery, and psychiatry. Quantitative and qualitative analyses were carried out. A language model based on n-grams was developed for the detection of errors, parameters for the selection of cases were defined, and a classification tool was implemented. The findings indicated that emergency medicine was the medical specialty with the highest number of context-dependent errors and that the most frequent type of error was omission of written accent. Furthermore, the analysis revealed the presence of errors of competence due to the incorrect application of the linguistic norm of Spanish, phenomena of phonetic similarity, and composition of words; it is also worth noting that performance errors occurred due to rapid typing on the keyboard. This study constituted the first analysis and creation of a typology of context-dependent errors for the medical domain in Spanish. It contributed to the design of a module based on linguistic knowledge that can be used for the development and improvement of automatic correction systems that, in turn, are used for data processing in medicine.

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Statistical Context-Sensitive Spelling Correction Using Typing Error Rate

Cited by 7 publications

References 4 publications

Error Checking for Chinese Query by Mining Web Log

Error Checking for Chinese Query by Mining Web Log

Towards Harnessing the Most of ChatGPT for Korean Grammatical Error Correction

Analysis of Context-Dependent Errors in the Medical Domain in Spanish: A Corpus-Based Study

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