Automatic Correction of Real-Word Errors in Spanish Clinical Texts
Real-word errors are characterized by being actual terms in the dictionary. By providing context, real-word errors are detected. Traditional methods to detect and correct such errors are mostly based on counting the frequency of short word sequences in a corpus. Then, the probability of a word being...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/9/2893 |
| _version_ | 1797536965450530816 |
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| author | Daniel Bravo-Candel Jésica López-Hernández José Antonio García-Díaz Fernando Molina-Molina Francisco García-Sánchez |
| author_facet | Daniel Bravo-Candel Jésica López-Hernández José Antonio García-Díaz Fernando Molina-Molina Francisco García-Sánchez |
| author_sort | Daniel Bravo-Candel |
| collection | DOAJ |
| description | Real-word errors are characterized by being actual terms in the dictionary. By providing context, real-word errors are detected. Traditional methods to detect and correct such errors are mostly based on counting the frequency of short word sequences in a corpus. Then, the probability of a word being a real-word error is computed. On the other hand, state-of-the-art approaches make use of deep learning models to learn context by extracting semantic features from text. In this work, a deep learning model were implemented for correcting real-word errors in clinical text. Specifically, a Seq2seq Neural Machine Translation Model mapped erroneous sentences to correct them. For that, different types of error were generated in correct sentences by using rules. Different Seq2seq models were trained and evaluated on two corpora: the Wikicorpus and a collection of three clinical datasets. The medicine corpus was much smaller than the Wikicorpus due to privacy issues when dealing with patient information. Moreover, GloVe and Word2Vec pretrained word embeddings were used to study their performance. Despite the medicine corpus being much smaller than the Wikicorpus, Seq2seq models trained on the medicine corpus performed better than those models trained on the Wikicorpus. Nevertheless, a larger amount of clinical text is required to improve the results. |
| first_indexed | 2024-03-10T12:08:20Z |
| format | Article |
| id | doaj.art-235f66c09b68440894ba0d6c798ef67e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T12:08:20Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-235f66c09b68440894ba0d6c798ef67e2023-11-21T16:25:01ZengMDPI AGSensors1424-82202021-04-01219289310.3390/s21092893Automatic Correction of Real-Word Errors in Spanish Clinical TextsDaniel Bravo-Candel0Jésica López-Hernández1José Antonio García-Díaz2Fernando Molina-Molina3Francisco García-Sánchez4Department of Informatics and Systems, Faculty of Computer Science, Campus de Espinardo, University of Murcia, 30100 Murcia, SpainDepartment of Informatics and Systems, Faculty of Computer Science, Campus de Espinardo, University of Murcia, 30100 Murcia, SpainDepartment of Informatics and Systems, Faculty of Computer Science, Campus de Espinardo, University of Murcia, 30100 Murcia, SpainVÓCALI Sistemas Inteligentes S.L., 30100 Murcia, SpainDepartment of Informatics and Systems, Faculty of Computer Science, Campus de Espinardo, University of Murcia, 30100 Murcia, SpainReal-word errors are characterized by being actual terms in the dictionary. By providing context, real-word errors are detected. Traditional methods to detect and correct such errors are mostly based on counting the frequency of short word sequences in a corpus. Then, the probability of a word being a real-word error is computed. On the other hand, state-of-the-art approaches make use of deep learning models to learn context by extracting semantic features from text. In this work, a deep learning model were implemented for correcting real-word errors in clinical text. Specifically, a Seq2seq Neural Machine Translation Model mapped erroneous sentences to correct them. For that, different types of error were generated in correct sentences by using rules. Different Seq2seq models were trained and evaluated on two corpora: the Wikicorpus and a collection of three clinical datasets. The medicine corpus was much smaller than the Wikicorpus due to privacy issues when dealing with patient information. Moreover, GloVe and Word2Vec pretrained word embeddings were used to study their performance. Despite the medicine corpus being much smaller than the Wikicorpus, Seq2seq models trained on the medicine corpus performed better than those models trained on the Wikicorpus. Nevertheless, a larger amount of clinical text is required to improve the results.https://www.mdpi.com/1424-8220/21/9/2893error correctionreal-word errorseq2seq neural machine translation modelclinical textsword embeddingsnatural language processing |
| spellingShingle | Daniel Bravo-Candel Jésica López-Hernández José Antonio García-Díaz Fernando Molina-Molina Francisco García-Sánchez Automatic Correction of Real-Word Errors in Spanish Clinical Texts Sensors error correction real-word error seq2seq neural machine translation model clinical texts word embeddings natural language processing |
| title | Automatic Correction of Real-Word Errors in Spanish Clinical Texts |
| title_full | Automatic Correction of Real-Word Errors in Spanish Clinical Texts |
| title_fullStr | Automatic Correction of Real-Word Errors in Spanish Clinical Texts |
| title_full_unstemmed | Automatic Correction of Real-Word Errors in Spanish Clinical Texts |
| title_short | Automatic Correction of Real-Word Errors in Spanish Clinical Texts |
| title_sort | automatic correction of real word errors in spanish clinical texts |
| topic | error correction real-word error seq2seq neural machine translation model clinical texts word embeddings natural language processing |
| url | https://www.mdpi.com/1424-8220/21/9/2893 |
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