Automated error detection through specialized task implementation
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Author (Corporation)
Publication date
2025
Type of student thesis
Course of study
Collections
Type
04B - Conference paper
Editor (Corporation)
Supervisor
Parent work
Pattern Recognition and Artificial Intelligence
Special issue
DOI of the original publication
Link
Related research data
Series
Lecture Notes in Computer Science
Series number
14893
Volume
Issue / Number
Pages / Duration
182-195
Patent number
Publisher / Publishing institution
Springer
Place of publication / Event location
Singapore
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
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.
Keywords
Subject (DDC)
Event
4th International Conference, ICPRAI 2024
Exhibition start date
Exhibition end date
Conference start date
03.07.2024
Conference end date
06.07.2024
Date of the last check
ISBN
978-981-97-8704-3
978-981-97-8705-0
978-981-97-8705-0
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
peer-reviewed
Open access category
Closed
License
Citation
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