Automated error detection through specialized task implementation
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Autor:in (Körperschaft)
Publikationsdatum
2025
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Pattern Recognition and Artificial Intelligence
Themenheft
DOI der Originalpublikation
Link
Zugehörige Forschungsdaten
Reihe / Serie
Lecture Notes in Computer Science
Reihennummer
14893
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
182-195
Patentnummer
Verlag / Herausgebende Institution
Springer
Verlagsort / Veranstaltungsort
Singapore
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
The present paper introduces a multilingual data set of erroneous and correct text sentences. The novel data set marks a significant advancement from an existing corpus by incorporating additional samples and refining its overall structure. The primary purpose of this data set is to support the research and development of automated error detection systems, especially in the multilingual setting where high-quality data sets are scarce. A distinctive feature of our data set is that it incorporates only incorrect sentences and their corresponding correct versions. These sentences are sourced from a variety of texts written by native speakers from different industries, such as pharmaceuticals, banking, insurance, retail, communications, and more. Each sentence in the data set has been annotated by professional proofreaders. The paper includes a comprehensive error analysis, where we classify and scrutinize the different types of errors within the data set. By categorizing and analysing the errors in the data set, we aim to identify patterns and common issues. Additionally, we conduct a thorough experimental evaluation using a well-established language model. Our analysis assesses the classification accuracy measured over all errors and the accuracy of each specific error type. Interestingly, our results show that while some error types can be detected with an accuracy exceeding 80%, it turns out that the recognition of other error types is very difficult to solve.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
4th International Conference, ICPRAI 2024
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
03.07.2024
Enddatum der Konferenz
06.07.2024
Datum der letzten Prüfung
ISBN
978-981-97-8704-3
978-981-97-8705-0
978-981-97-8705-0
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
peer-reviewed
Open Access-Status
Closed
Lizenz
Zitation
Masanti, C., Witschel, H. F., & Riesen, K. (2025). Automated error detection through specialized task implementation. In C. Wallraven, C.-L. Liu, & A. Ross (Eds.), Pattern Recognition and Artificial Intelligence (pp. 182–195). Springer. https://doi.org/10.1007/978-981-97-8705-0_12