@inproceedings{mishra-mishra-2020-scubed-3c,
title = "Scubed at 3{C} task {B} - A simple baseline for citation context influence classification",
author = "Mishra, Shubhanshu and
Mishra, Sudhanshu",
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-1.10/",
pages = "65--70",
abstract = "We present our team Scubed`s approach in the 3C Citation Context Classification Task, Subtask B, citation context influence classification. Our approach relies on text based features transformed via tf-idf features followed by training a variety of simple models resulting in a strong baseline. Our best model on the leaderboard is a random forest classifier using only the citation context text. A replication of our analysis finds logistic regression and gradient boosted tree classifier to be the best performing model. Our submission code can be found at: \url{https://github.com/napsternxg/Citation_Context_Classification}."
}
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<abstract>We present our team Scubed‘s approach in the 3C Citation Context Classification Task, Subtask B, citation context influence classification. Our approach relies on text based features transformed via tf-idf features followed by training a variety of simple models resulting in a strong baseline. Our best model on the leaderboard is a random forest classifier using only the citation context text. A replication of our analysis finds logistic regression and gradient boosted tree classifier to be the best performing model. Our submission code can be found at: https://github.com/napsternxg/Citation_Context_Classification.</abstract>
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%0 Conference Proceedings
%T Scubed at 3C task B - A simple baseline for citation context influence classification
%A Mishra, Shubhanshu
%A Mishra, Sudhanshu
%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 mishra-mishra-2020-scubed-3c
%X We present our team Scubed‘s approach in the 3C Citation Context Classification Task, Subtask B, citation context influence classification. Our approach relies on text based features transformed via tf-idf features followed by training a variety of simple models resulting in a strong baseline. Our best model on the leaderboard is a random forest classifier using only the citation context text. A replication of our analysis finds logistic regression and gradient boosted tree classifier to be the best performing model. Our submission code can be found at: https://github.com/napsternxg/Citation_Context_Classification.
%U https://aclanthology.org/2020.wosp-1.10/
%P 65-70
Markdown (Informal)
[Scubed at 3C task B - A simple baseline for citation context influence classification](https://aclanthology.org/2020.wosp-1.10/) (Mishra & Mishra, WOSP 2020)
ACL