@inproceedings{sharma-etal-2021-drift,
title = "{DRIFT}: A Toolkit for Diachronic Analysis of Scientific Literature",
author = "Sharma, Abheesht and
Chhablani, Gunjan and
Pandey, Harshit and
Patil, Rajaswa",
editor = "Adel, Heike and
Shi, Shuming",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-demo.40",
doi = "10.18653/v1/2021.emnlp-demo.40",
pages = "361--371",
abstract = "In this work, we present to the NLP community, and to the wider research community as a whole, an application for the diachronic analysis of research corpora. We open source an easy-to-use tool coined DRIFT, which allows researchers to track research trends and development over the years. The analysis methods are collated from well-cited research works, with a few of our own methods added for good measure. Succinctly put, some of the analysis methods are: keyword extraction, word clouds, predicting declining/stagnant/growing trends using Productivity, tracking bi-grams using Acceleration plots, finding the Semantic Drift of words, tracking trends using similarity, etc. To demonstrate the utility and efficacy of our tool, we perform a case study on the cs.CL corpus of the arXiv repository and draw inferences from the analysis methods. The toolkit and the associated code are available here: \url{https://github.com/rajaswa/DRIFT}.",
}
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%0 Conference Proceedings
%T DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature
%A Sharma, Abheesht
%A Chhablani, Gunjan
%A Pandey, Harshit
%A Patil, Rajaswa
%Y Adel, Heike
%Y Shi, Shuming
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F sharma-etal-2021-drift
%X In this work, we present to the NLP community, and to the wider research community as a whole, an application for the diachronic analysis of research corpora. We open source an easy-to-use tool coined DRIFT, which allows researchers to track research trends and development over the years. The analysis methods are collated from well-cited research works, with a few of our own methods added for good measure. Succinctly put, some of the analysis methods are: keyword extraction, word clouds, predicting declining/stagnant/growing trends using Productivity, tracking bi-grams using Acceleration plots, finding the Semantic Drift of words, tracking trends using similarity, etc. To demonstrate the utility and efficacy of our tool, we perform a case study on the cs.CL corpus of the arXiv repository and draw inferences from the analysis methods. The toolkit and the associated code are available here: https://github.com/rajaswa/DRIFT.
%R 10.18653/v1/2021.emnlp-demo.40
%U https://aclanthology.org/2021.emnlp-demo.40
%U https://doi.org/10.18653/v1/2021.emnlp-demo.40
%P 361-371
Markdown (Informal)
[DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature](https://aclanthology.org/2021.emnlp-demo.40) (Sharma et al., EMNLP 2021)
ACL
- Abheesht Sharma, Gunjan Chhablani, Harshit Pandey, and Rajaswa Patil. 2021. DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 361–371, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.