TY - GEN
T1 - Paperswithtopic
T2 - 6th Asian Conference on Pattern Recognition, ACPR 2021
AU - Cho, Daehyun
AU - Wallraven, Christian
N1 - Funding Information:
Acknowledgments. This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grants funded by the Korean government (MSIT) (No. 2019-0-00079, Department of Artificial Intelligence, Korea University; No. 2021-0-02068-001, Artificial Intelligence Innovation Hub).
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The deep learning field is growing rapidly as witnessed by the exponential growth of papers submitted to journals, conferences, and pre-print servers. To cope with the sheer number of papers, several text mining tools from natural language processing (NLP) have been proposed that enable researchers to keep track of recent findings. In this context, our paper makes two main contributions: first, we collected and annotated a dataset of papers paired by title and sub-field from the field of artificial intelligence (AI), and, second, we present results on how to predict a paper’s AI sub-field from a given paper title only. Importantly, for the latter, short-text classification task we compare several algorithms from conventional machine learning all the way up to recent, larger transformer architectures. Finally, for the transformer models, we also present gradient-based, attention visualizations to further explain the model’s classification process. All code can be found online (Code available here: https://github.com/1pha/paperswithtopic ).
AB - The deep learning field is growing rapidly as witnessed by the exponential growth of papers submitted to journals, conferences, and pre-print servers. To cope with the sheer number of papers, several text mining tools from natural language processing (NLP) have been proposed that enable researchers to keep track of recent findings. In this context, our paper makes two main contributions: first, we collected and annotated a dataset of papers paired by title and sub-field from the field of artificial intelligence (AI), and, second, we present results on how to predict a paper’s AI sub-field from a given paper title only. Importantly, for the latter, short-text classification task we compare several algorithms from conventional machine learning all the way up to recent, larger transformer architectures. Finally, for the transformer models, we also present gradient-based, attention visualizations to further explain the model’s classification process. All code can be found online (Code available here: https://github.com/1pha/paperswithtopic ).
KW - Deep learning
KW - Model comparison
KW - Natural language processing
KW - Sequence classification
UR - http://www.scopus.com/inward/record.url?scp=85130298236&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-02444-3_19
DO - 10.1007/978-3-031-02444-3_19
M3 - Conference contribution
AN - SCOPUS:85130298236
SN - 9783031024436
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 254
EP - 267
BT - Pattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers
A2 - Wallraven, Christian
A2 - Liu, Qingshan
A2 - Nagahara, Hajime
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 9 November 2021 through 12 November 2021
ER -