Context-aware correction of spelling errors in Hungarian medical documents

Siklósi, Borbála; Novák, Attila; Prószéky, Gábor

doi:10.1016/j.csl.2014.09.001

Cited by 12 publications

(6 citation statements)

References 5 publications

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“…Our attempt to address the problem of lexical simplification, and, in the long run, improve readability of Swedish EHRs, by automatically detecting and resolving out of dictionary words, achieves 91.1% (abbreviations), 83.5% (compound splitting) and 83.87% (spelling correction) precision, respectively. These results are comparable to those reported in similar studies on English and Hungarian patient records (Patrick et al, 2010;Siklósi et al, 2013).…”

Section: Discussionsupporting

confidence: 92%

Improving Readability of Swedish Electronic Health Records through Lexical Simplification: First Results

Grigonytė

Kvist

Velupillai

et al. 2014

Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR)

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This paper describes part of an ongoing effort to improve the readability of Swedish electronic health records (EHRs). An EHR contains systematic documentation of a single patient's medical history across time, entered by healthcare professionals with the purpose of enabling safe and informed care. Linguistically, medical records exemplify a highly specialised domain, which can be superficially characterised as having telegraphic sentences involving displaced or missing words, abundant abbreviations, spelling variations including misspellings, and terminology. We report results on lexical simplification of Swedish EHRs, by which we mean detecting the unknown, out-ofdictionary words and trying to resolve them either as compounded known words, abbreviations or misspellings.

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Section: Discussionsupporting

confidence: 92%

Improving Readability of Swedish Electronic Health Records through Lexical Simplification: First Results

Grigonytė

Kvist

Velupillai

et al. 2014

Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR)

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“…Therefore, fluctuations in the results are observed depending on the language and environment. In addition, these authors point out that the most common error patterns involve the addition, omission and transposition of characters, the inappropriate use of punctuation marks, grammatical errors, and overuse of abbreviated forms (Siklósi et al, 2016). According to the studies for non-word or spelling errors by Damerau (1964), Kukich (1992), Ramírez (2006), Gimenes (2015), and López-Hernández and Almela (2021), most of the errors present in the corpus tend to be unique cases of error in the word.…”

Section: Discussionmentioning

confidence: 99%

“…Various investigations have been carried out on automatic detection and correction in clinical documentation and, specifically, in clinical reports. Most of these studies have been developed with corpora in English (e.g., Fivez et al, 2017;Workman et al, 2019), but these issues have been also explored for French (D'Hondt et al, 2016), Russian (Balabaeva et al, 2020), Swedish (Dziadek et al, 2017), Dutch (Fivez et al, 2017), Hungarian (Siklo´si et al, 2016), and Persian (Yazdani et al, 2020). These works highlight the substantial number of linguistic errors that the corpora comprising clinical reports usually contain, with studies whose error rate is around 5% (Lai et al, 2015) or even 10% (Ruch et al, 2003).…”

Section: Error Analysis and Automatic Correction In The Medical Domainmentioning

confidence: 99%

“…Among the most widely used detection and correction techniques, we must highlight dictionary search and editing distance, which appear in all the studies revised by the present authors. Likewise, the use of statistical methods and the analysis of n-grams stand out (Lu & Demner-Fushman, 2018; Siklósi et al, 2016; Wong & Glance, 2011). Some studies also make use of methods based on rules and regular expressions (Lai et al, 2015; Thompson et al, 2015); others incorporate POS-tagging (Hussain & Qamar, 2016; Zhou et al, 2015); or the use of ontologies (Dziadek et al, 2017).…”

Section: Error Analysis and Automatic Correction In The Medical Domainmentioning

confidence: 99%

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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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“…They prevent word segmentation errors and misspelling errors by including all confusing characters in the word grid to improve the accuracy of rule matching. Siklósi et al [13] proposed a method for automatically correcting spelling errors in Hungarian clinical records. They modeled the spelling correction problem as a translation task, using the error text as the source language, the target text as the corrected text, using the statistical machine translation model to perform the error correction task, and modeling the lexical context using a 3-gram-based language model.…”

Section: Introductionmentioning

confidence: 99%

Post Text Processing of Chinese Speech Recognition Based on Bidirectional LSTM Networks and CRF

et al. 2019

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With the rapid development of Internet of Things Technology, speech recognition has been applied more and more widely. Chinese Speech Recognition is a complex process. In the process of speech-to-text conversion, due to the influence of dialect, environmental noise, and context, the accuracy of speech-to-text in multi-round dialogues and specific contexts is still not high. After the general speech recognition technology, the text after speech recognition can be detected and corrected in the specific context, which is helpful to improve the robustness of text comprehension and is a beneficial supplement to the speech recognition technology. In this paper, a text processing model after Chinese Speech Recognition is proposed, which combines a bidirectional long short-term memory (LSTM) network with a conditional random field (CRF) model. The task is divided into two stages: text error detection and text error correction. In this paper, a bidirectional long short-term memory (Bi-LSTM) network and conditional random field are used in two stages of text error detection and text error correction respectively. Through verification and system test on the SIGHAN 2013 Chinese Spelling Check (CSC) dataset, the experimental results show that the model can effectively improve the accuracy of text after speech recognition.

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Context-aware correction of spelling errors in Hungarian medical documents

Cited by 12 publications

References 5 publications

Improving Readability of Swedish Electronic Health Records through Lexical Simplification: First Results

Improving Readability of Swedish Electronic Health Records through Lexical Simplification: First Results

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

Post Text Processing of Chinese Speech Recognition Based on Bidirectional LSTM Networks and CRF

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