Radiologic text correction for better machine understanding
Abstract Radiologic reports often contain misspellings that compromise report quality and pose challenges for machine understanding methods, which require syntactical correctness. General automatic misspell correction solutions are less effective in specialized documents, such as spinal radiologic r...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.12891 |
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| _version_ | 1850256014049280000 |
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| author | András Kicsi Klaudia Szabó Ledenyi László Vidács |
| author_facet | András Kicsi Klaudia Szabó Ledenyi László Vidács |
| author_sort | András Kicsi |
| collection | DOAJ |
| description | Abstract Radiologic reports often contain misspellings that compromise report quality and pose challenges for machine understanding methods, which require syntactical correctness. General automatic misspell correction solutions are less effective in specialized documents, such as spinal radiologic reports, particularly in morphologically rich languages like Hungarian. Issues arise from complex conjugations and the modification of Latin terms per the rules of the native language. This study introduces a method for the automatic correction of these misspellings, utilizing the Hunspell software and field‐specific dictionaries. This approach, enhanced by linguistic analysis and statistical data, improves information retrieval, as demonstrated in machine‐learning‐based classification and rule‐based identification tasks. Notably, our method identified over 30% more valid errors than human annotators, highlighting its efficiency. We offer a primarily dictionary‐based solution for correcting highly specialized texts and explore the impact of nonword correction on machine understanding. This work underscores the significance of tailored spelling correction in enhancing text processing algorithms' accuracy. |
| format | Article |
| id | doaj-art-2b1f37bf303e4634b716ffce63ee2221 |
| institution | OA Journals |
| issn | 2577-8196 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-2b1f37bf303e4634b716ffce63ee22212025-08-20T01:56:45ZengWileyEngineering Reports2577-81962024-12-01612n/an/a10.1002/eng2.12891Radiologic text correction for better machine understandingAndrás Kicsi0Klaudia Szabó Ledenyi1László Vidács2Department of Software Engineering University of Szeged Szeged HungaryDepartment of Software Engineering University of Szeged Szeged HungaryDepartment of Software Engineering University of Szeged Szeged HungaryAbstract Radiologic reports often contain misspellings that compromise report quality and pose challenges for machine understanding methods, which require syntactical correctness. General automatic misspell correction solutions are less effective in specialized documents, such as spinal radiologic reports, particularly in morphologically rich languages like Hungarian. Issues arise from complex conjugations and the modification of Latin terms per the rules of the native language. This study introduces a method for the automatic correction of these misspellings, utilizing the Hunspell software and field‐specific dictionaries. This approach, enhanced by linguistic analysis and statistical data, improves information retrieval, as demonstrated in machine‐learning‐based classification and rule‐based identification tasks. Notably, our method identified over 30% more valid errors than human annotators, highlighting its efficiency. We offer a primarily dictionary‐based solution for correcting highly specialized texts and explore the impact of nonword correction on machine understanding. This work underscores the significance of tailored spelling correction in enhancing text processing algorithms' accuracy.https://doi.org/10.1002/eng2.12891information retrievalmedical reportsmisspell correctionnatural language processing |
| spellingShingle | András Kicsi Klaudia Szabó Ledenyi László Vidács Radiologic text correction for better machine understanding Engineering Reports information retrieval medical reports misspell correction natural language processing |
| title | Radiologic text correction for better machine understanding |
| title_full | Radiologic text correction for better machine understanding |
| title_fullStr | Radiologic text correction for better machine understanding |
| title_full_unstemmed | Radiologic text correction for better machine understanding |
| title_short | Radiologic text correction for better machine understanding |
| title_sort | radiologic text correction for better machine understanding |
| topic | information retrieval medical reports misspell correction natural language processing |
| url | https://doi.org/10.1002/eng2.12891 |
| work_keys_str_mv | AT andraskicsi radiologictextcorrectionforbettermachineunderstanding AT klaudiaszaboledenyi radiologictextcorrectionforbettermachineunderstanding AT laszlovidacs radiologictextcorrectionforbettermachineunderstanding |