RailwayReq Corpus
Authors/Creators
- 1. Graz University of Technology
-
2.
Drexel University
Description
Please cite as: Perko, A., Zhao, H. & Wotawa, F. (2023). Optimizing Named Entity Recognition for Improving Logical Formulae Abstraction from Technical Requirements Documents. In 2023 10th International Conference on Dependable Systems and Their Applications (DSA) (pp. 211-222). IEEE.
https://ieeexplore.ieee.org/document/10314370
Dataset published alongside the paper: "Optimizing Named Entity Recognition for Improving Logical Formulae Abstraction from Technical Requirements Documents". This is a domain-specific NER corpus compiled from technical requirements documents published by the European Unions' railway agency [1], which are also part of the PURE data set of publicly available requirements documents [2]. This corpus was annotated to extract named entities for the generation of predicate-argument structres as used in logical formalisms.
[1] European Union agency for railways. URL https://www.era.europa.eu
[2] Ferrari, A., Spagnolo, G. O., & Gnesi, S. (2017, September). PURE: A dataset of public requirements documents. In 2017 IEEE 25th International Requirements Engineering Conference (RE) (pp. 502-505). IEEE.
Files
RailwayReq_Corpus.zip
Files
(96.9 kB)
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Additional details
Additional titles
- Other (English)
- Dataset published alongside: "Optimizing Named Entity Recognition for Improving Logical Formulae Abstraction from Technical Requirements Documents"
Related works
- Is derived from
- Dataset: 10.5281/zenodo.1414116 (DOI)
- Is described by
- Conference paper: 10.1109/DSA59317.2023.00034 (DOI)