Supplementary Material For Our PROPSER21 Paper
Supplementary Material for the paper by Angelika Kaplan and Jan Keim with the title 'Towards an Automated Classification Approach for Software Engineering Research'.
Cite
@inproceedings{10.1145/3463274.3463358,
author = {Kaplan, Angelika and Keim, Jan},
title = {Towards an Automated Classification Approach for Software Engineering Research},
year = {2021},
isbn = {9781450390538},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3463274.3463358},
doi = {10.1145/3463274.3463358},
abstract = { The rapid growth of software engineering research publications forces an amount of scholarly knowledge that needs to be managed, organized and communicated in digital libraries and scientific search engines. Thus, there is a need for classified papers to accomplish these tasks, but the classification process is cumbersome. Moreover, in case of new schemas, one would need to reclassify previously published research. We propose to automate the classification and present different possible techniques for doing so: Using natural language models, a rule-based approach, or an approach based on topic-labeling. In this proposal paper, we initially implemented a prototype for text classification of software engineering research papers.},
booktitle = {Evaluation and Assessment in Software Engineering},
pages = {347–352},
numpages = {6},
keywords = {scholarly knowledge communication, neural machine learning, information extraction, Research knowledge organization and management, NLP, text classification},
location = {Trondheim, Norway},
series = {EASE 2021}
}