@inproceedings{ikeda-etal-2020-normalized,
title = "The Normalized Impact Index for Keywords in Scholarly Papers to Detect Subtle Research Topics",
author = "Ikeda, Daisuke and
Taniguchi, Yuta and
Koga, Kazunori",
editor = "Knoth, Petr and
Stahl, Christopher and
Gyawali, Bikash and
Pride, David and
Kunnath, Suchetha N. and
Herrmannova, Drahomira",
booktitle = "Proceedings of the 8th International Workshop on Mining Scientific Publications",
month = "05 " # aug,
year = "2020",
address = "Wuhan, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wosp-1.6",
pages = "42--47",
abstract = "Mainly due to the open access movement, the number of scholarly papers we can freely access is drastically increasing. A huge amount of papers is a promising resource for text mining and machine learning. Given a set of papers, for example, we can grasp past or current trends in a research community. Compared to the trend detection, it is more difficult to forecast trends in the near future, since the number of occurrences of some features, which are major cues for automatic detection, such as the word frequency, is quite small before such a trend will emerge. As a first step toward trend forecasting, this paper is devoted to finding subtle trends. To do this, the authors propose an index for keywords, called normalized impact index, and visualize keywords and their indices as a heat map. The authors have conducted case studies using some keywords already known as popular, and we found some keywords whose frequencies are not so large but whose indices are large.",
}
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%0 Conference Proceedings
%T The Normalized Impact Index for Keywords in Scholarly Papers to Detect Subtle Research Topics
%A Ikeda, Daisuke
%A Taniguchi, Yuta
%A Koga, Kazunori
%Y Knoth, Petr
%Y Stahl, Christopher
%Y Gyawali, Bikash
%Y Pride, David
%Y Kunnath, Suchetha N.
%Y Herrmannova, Drahomira
%S Proceedings of the 8th International Workshop on Mining Scientific Publications
%D 2020
%8 05 aug
%I Association for Computational Linguistics
%C Wuhan, China
%F ikeda-etal-2020-normalized
%X Mainly due to the open access movement, the number of scholarly papers we can freely access is drastically increasing. A huge amount of papers is a promising resource for text mining and machine learning. Given a set of papers, for example, we can grasp past or current trends in a research community. Compared to the trend detection, it is more difficult to forecast trends in the near future, since the number of occurrences of some features, which are major cues for automatic detection, such as the word frequency, is quite small before such a trend will emerge. As a first step toward trend forecasting, this paper is devoted to finding subtle trends. To do this, the authors propose an index for keywords, called normalized impact index, and visualize keywords and their indices as a heat map. The authors have conducted case studies using some keywords already known as popular, and we found some keywords whose frequencies are not so large but whose indices are large.
%U https://aclanthology.org/2020.wosp-1.6
%P 42-47
Markdown (Informal)
[The Normalized Impact Index for Keywords in Scholarly Papers to Detect Subtle Research Topics](https://aclanthology.org/2020.wosp-1.6) (Ikeda et al., WOSP 2020)
ACL