Paperswithtopic: Topic Identification from Paper Title Only

Daehyun Cho, Christian Wallraven

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 ).

Original languageEnglish
Title of host publicationPattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers
EditorsChristian Wallraven, Qingshan Liu, Hajime Nagahara
PublisherSpringer Science and Business Media Deutschland GmbH
Pages254-267
Number of pages14
ISBN (Print)9783031024436
DOIs
Publication statusPublished - 2022
Event6th Asian Conference on Pattern Recognition, ACPR 2021 - Virtual, Online
Duration: 2021 Nov 92021 Nov 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13189 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th Asian Conference on Pattern Recognition, ACPR 2021
CityVirtual, Online
Period21/11/921/11/12

Bibliographical note

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.

Keywords

  • Deep learning
  • Model comparison
  • Natural language processing
  • Sequence classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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