@inproceedings{kobs-etal-2020-submit,
title = "Where to Submit? Helping Researchers to Choose the Right Venue",
author = "Kobs, Konstantin and
Koopmann, Tobias and
Zehe, Albin and
Fernes, David and
Krop, Philipp and
Hotho, Andreas",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.78/",
doi = "10.18653/v1/2020.findings-emnlp.78",
pages = "878--883",
abstract = "Whenever researchers write a paper, the same question occurs: {\textquotedblleft}Where to submit?{\textquotedblright} In this work, we introduce WTS, an open and interpretable NLP system that recommends conferences and journals to researchers based on the title, abstract, and/or keywords of a given paper. We adapt the TextCNN architecture and automatically analyze its predictions using the Integrated Gradients method to highlight words and phrases that led to the recommendation of a scientific venue. We train and test our method on publications from the fields of artificial intelligence (AI) and medicine, both derived from the Semantic Scholar dataset. WTS achieves an Accuracy@5 of approximately 83{\%} for AI papers and 95{\%} in the field of medicine. It is open source and available for testing on \url{https://wheretosubmit.ml}."
}
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<abstract>Whenever researchers write a paper, the same question occurs: “Where to submit?” In this work, we introduce WTS, an open and interpretable NLP system that recommends conferences and journals to researchers based on the title, abstract, and/or keywords of a given paper. We adapt the TextCNN architecture and automatically analyze its predictions using the Integrated Gradients method to highlight words and phrases that led to the recommendation of a scientific venue. We train and test our method on publications from the fields of artificial intelligence (AI) and medicine, both derived from the Semantic Scholar dataset. WTS achieves an Accuracy@5 of approximately 83% for AI papers and 95% in the field of medicine. It is open source and available for testing on https://wheretosubmit.ml.</abstract>
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%0 Conference Proceedings
%T Where to Submit? Helping Researchers to Choose the Right Venue
%A Kobs, Konstantin
%A Koopmann, Tobias
%A Zehe, Albin
%A Fernes, David
%A Krop, Philipp
%A Hotho, Andreas
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F kobs-etal-2020-submit
%X Whenever researchers write a paper, the same question occurs: “Where to submit?” In this work, we introduce WTS, an open and interpretable NLP system that recommends conferences and journals to researchers based on the title, abstract, and/or keywords of a given paper. We adapt the TextCNN architecture and automatically analyze its predictions using the Integrated Gradients method to highlight words and phrases that led to the recommendation of a scientific venue. We train and test our method on publications from the fields of artificial intelligence (AI) and medicine, both derived from the Semantic Scholar dataset. WTS achieves an Accuracy@5 of approximately 83% for AI papers and 95% in the field of medicine. It is open source and available for testing on https://wheretosubmit.ml.
%R 10.18653/v1/2020.findings-emnlp.78
%U https://aclanthology.org/2020.findings-emnlp.78/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.78
%P 878-883
Markdown (Informal)
[Where to Submit? Helping Researchers to Choose the Right Venue](https://aclanthology.org/2020.findings-emnlp.78/) (Kobs et al., Findings 2020)
- Where to Submit? Helping Researchers to Choose the Right Venue (Kobs et al., Findings 2020)
ACL
- Konstantin Kobs, Tobias Koopmann, Albin Zehe, David Fernes, Philipp Krop, and Andreas Hotho. 2020. Where to Submit? Helping Researchers to Choose the Right Venue. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 878–883, Online. Association for Computational Linguistics.