Papers › Biomedical NER using Novel Schema and Distant Supervision
Biomedical NER using Novel Schema and Distant Supervision
Anshita Khandelwal, Alok Kar, Veera Raghavendra Chikka, Kamalakar Karlapalem
Biomedical Named Entity Recognition (BMNER) is one of the most important tasks in the field of biomedical text mining. Most work so far on this task has not focused on identification of discontinuous and overlapping entities, even though they are present in significant fractions in real-life biomedical datasets. In this paper, we introduce a novel annotation schema to capture complex entities, and explore the effects of distant supervision on our deep-learning sequence labelling model. For BMNER task, our annotation schema outperforms other BIO-based annotation schemes on the same model. We also achieve higher F1-scores than state-of-the-art models on multiple corpora without fine-tuning embeddings, highlighting the efficacy of neural feature extraction using our model.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Medical Named Entity Recognition | ShARe/CLEF 2014 Task 2 Disorders | Distant Supervision with BIODT Tagging | F1 | 0.807 | #1 of 1 | Archive leaderboard | report |
| Medical Named Entity Recognition | ShARe/CLEF eHealth corpus | Distant Supervision with BIODT Tagging | F1 | 0.799 | #2 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections