Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Database - The journal of Biological Databases and Curation Année : 2022

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations

Jingcheng Du
Li Fang
Kai Wang
  • Fonction : Auteur
Shuo Xu
Yuefu Zhang
  • Fonction : Auteur
Parsa Bagherzadeh
  • Fonction : Auteur
Sabine Bergler
  • Fonction : Auteur
Aakash Bhatnagar
  • Fonction : Auteur
Nidhir Bhavsar
  • Fonction : Auteur
Yung-Chun Chang
Sheng-Jie Lin
  • Fonction : Auteur
Wentai Tang
  • Fonction : Auteur
Hongtong Zhang
  • Fonction : Auteur
Ilija Tavchioski
  • Fonction : Auteur
Senja Pollak
  • Fonction : Auteur
Shubo Tian
Jinfeng Zhang
Yulia Otmakhova
  • Fonction : Auteur
Antonio Jimeno Yepes
  • Fonction : Auteur
Hang Dong
  • Fonction : Auteur
Honghan Wu
Niladri Chatterjee
  • Fonction : Auteur
Kushagri Tandon
  • Fonction : Auteur
Fréjus Laleye
  • Fonction : Auteur
Loïc Rakotoson
Emmanuele Chersoni
  • Fonction : Auteur
Jinghang Gu
  • Fonction : Auteur
Annemarie Friedrich
  • Fonction : Auteur
Subhash Chandra Pujari
  • Fonction : Auteur
Mariia Chizhikova
  • Fonction : Auteur
Naveen Sivadasan
  • Fonction : Auteur
Saipradeep Vg
  • Fonction : Auteur

Résumé

Abstract The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature—at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset—consisting of over 30 000 articles with manually reviewed topics—was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/

Dates et versions

hal-03767225 , version 1 (01-09-2022)

Identifiants

Citer

Qingyu Chen, Alexis Allot, Robert Leaman, Rezarta Islamaj, Jingcheng Du, et al.. Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations. Database - The journal of Biological Databases and Curation, 2022, 2022, ⟨10.1093/database/baac069⟩. ⟨hal-03767225⟩
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