Automatic Correction of Real-Word Errors in Spanish Clinical Texts

Bravo-Candel, Daniel; López-Hernández, Jésica; Garcí­a-Dí­az, José Antonio; Molina-Molina, Fernando; García‐Sánchez, Francisco

doi:10.3390/s21092893

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…The same part-of-speech-tagger used in our main analyses was employed to find all verbs in each preprocessed retelling. Then, the numerical representation of all verbs in each retelling was obtained using a previously reported GloVe model, pre-trained with the Wikipedia 2018 Corpus, which contains ≈709 million Spanish words 59 . We computed the cosine distance between each verb in the retelling and the verbs in the original story (i.e., the same verbs used in our main analyses).…”

Section: Methodsmentioning

confidence: 99%

Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories

García

Escobar-Grisales

Correa

et al. 2022

npj Parkinsons Dis.

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Action-concept outcomes are useful targets to identify Parkinson’s disease (PD) patients and differentiate between those with and without mild cognitive impairment (PD-MCI, PD-nMCI). Yet, most approaches employ burdensome examiner-dependent tasks, limiting their utility. We introduce a framework capturing action-concept markers automatically in natural speech. Patients from both subgroups and controls retold an action-laden and a non-action-laden text (AT, nAT). In each retelling, we weighed action and non-action concepts through our automated Proximity-to-Reference-Semantic-Field (P-RSF) metric, for analysis via ANCOVAs (controlling for cognitive dysfunction) and support vector machines. Patients were differentiated from controls based on AT (but not nAT) P-RSF scores. The same occurred in PD-nMCI patients. Conversely, PD-MCI patients exhibited reduced P-RSF scores for both texts. Direct discrimination between patient subgroups was not systematic, but it yielded best outcomes via AT scores. Our approach outperformed classifiers based on corpus-derived embeddings. This framework opens scalable avenues to support PD diagnosis and phenotyping.

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Section: Methodsmentioning

confidence: 99%

Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories

García

Escobar-Grisales

Correa

et al. 2022

npj Parkinsons Dis.

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show abstract

“…In the case of Spanish, Bravo-Candel et al (2021) proposes the use of a neural machine translation seq2seq model to correct these errors. The corpus used to train and evaluate the model was compiled from Wikicorpus and a medical corpus made up of clinical cases from CodiEsp, MEDDOCAN, and SPACC.…”

Section: Error Analysis and Automatic Correction In The Medical Domainmentioning

confidence: 99%

“…There is no previous research analyzing the presence of this type of errors in clinical texts in Spanish. Currently, only Bravo-Candel et al (2021) implements a model based on deep learning to correct context-dependent errors in clinical texts in Spanish. This study confirms the need to better know the variability of the errors that appear in this domain for improvement.…”

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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“…Candel et al propose an approach for correcting real-word errors in clinical text [15]. A sequence-to-sequence neural machine translation method is implemented which maps the misspelled sentences to correct them.…”

Section: Literature Reviewmentioning

confidence: 99%

Real Word Spelling Error Detection and Correction for Urdu Language

Aziz,

Anwar,

Jamal

et al. 2023

IEEE Access

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Non-word and real-word errors are generally two types of spelling errors. Non-word errors are misspelled words that are nonexistent in the lexicon while real-word errors are misspelled words that exist in the lexicon but are used out of context in a sentence. Lexicon-based lookup approach is widely used for non-word errors but it is incapable of handling real-word errors as they require contextual information. Contrary to the English language, real-word error detection and correction for low-resourced languages like Urdu is an unexplored area. This paper presents a real-word spelling error detection and correction approach for the Urdu language. We develop an extensive lexicon of 593,738 words and use this lexicon to develop a dataset for real-word errors comprising 125562 sentences and 2,552,735 words. Based on the developed lexicon and dataset, we then develop a contextual spell checker that detects and corrects real-word errors. For the real-word error detection phase, word-gram features are used along with five machine learning classifiers, achieving a precision, recall, and F1-score of 0.84,0.79, and 0.81 respectively. We also test the proposed approach with a 40% error density. For real-word error correction, the Damerau-Levenshtein distance is used along with the n-gram model for further ranking of the suggested candidate words, achieving an accuracy of up to 83.67%.

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Automatic Correction of Real-Word Errors in Spanish Clinical Texts

Cited by 9 publications

References 28 publications

Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories

Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories

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

Real Word Spelling Error Detection and Correction for Urdu Language

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