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Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers

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Jiawei HanAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 3458 - 3469

https://doi.org/10.1145/3580305.3599544

Published: 04 August 2023 Publication History

Abstract

Instead of relying on human-annotated training samples to build a classifier, weakly supervised scientific paper classification aims to classify papers only using category descriptions (e.g., category names, category-indicative keywords). Existing studies on weakly supervised paper classification are less concerned with two challenges: (1) Papers should be classified into not only coarse-grained research topics but also fine-grained themes, and potentially into multiple themes, given a large and fine-grained label space; and (2) full text should be utilized to complement the paper title and abstract for classification. Moreover, instead of viewing the entire paper as a long linear sequence, one should exploit the structural information such as citation links across papers and the hierarchy of sections and paragraphs in each paper. To tackle these challenges, in this study, we propose FUTEX, a framework that uses the cross-paper network structure and the in-paper hierarchy structure to classify full-text scientific papers under weak supervision. A network-aware contrastive fine-tuning module and a hierarchy-aware aggregation module are designed to leverage the two types of structural signals, respectively. Experiments on two benchmark datasets demonstrate that FUTEX significantly outperforms competitive baselines and is on par with fully supervised classifiers that use 1,000 to 60,000 ground-truth training samples.

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Cited By

Curiac CMicea MPlosca TCuriac DDoboli SDoboli A(2024)Automating Research Problem Framing and Exploration through Knowledge Extraction from Bibliometric DataBibliometrics - An Essential Methodological Tool for Research Projects [Working Title]10.5772/intechopen.1005575Online publication date: 10-Jun-2024
https://doi.org/10.5772/intechopen.1005575

Index Terms

Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

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Published: 04 August 2023

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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 6 - 10, 2023

CA, Long Beach, USA

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Cited By

Curiac CMicea MPlosca TCuriac DDoboli SDoboli A(2024)Automating Research Problem Framing and Exploration through Knowledge Extraction from Bibliometric DataBibliometrics - An Essential Methodological Tool for Research Projects [Working Title]10.5772/intechopen.1005575Online publication date: 10-Jun-2024
https://doi.org/10.5772/intechopen.1005575

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