Demo Data from EDBT 2026 / reviewers in the wild / expert
Tejasri Kopparthi
dblp:362/7913
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Information extraction and text analysis · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › entity linking
biomedical entity linking |
0.7 | 1 | 2023 | A Comprehensive Evaluation of Biomedical Entity Linking Models · EMNLP 2023 |
Natural language and speech › Information extraction and text analysis
entity linking |
0.7 | 1 | 2023 | A Comprehensive Evaluation of Biomedical Entity Linking Models · EMNLP 2023 |
Bioinformatics and computational biology › biomedical text mining
biomedical entity linking |
0.7 | 1 | 2023 | A Comprehensive Evaluation of Biomedical Entity Linking Models · EMNLP 2023 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | A Comprehensive Evaluation of Biomedical Entity Linking ModelsabstractBiomedical entity linking (BioEL) is the process of connecting entities referenced in documents to entries in biomedical databases such as the Unified Medical Language System (UMLS) or Medical Subject Headings (MeSH). The study objective was to comprehensively evaluate nine recent state-of-the-art biomedical entity linking models under a unified framework. We compare these models along axes of (1) accuracy, (2) speed, (3) ease of use, (4) generalization, and (5) adaptability to new ontologies and datasets. We additionally quantify the impact of various preprocessing choices such as abbreviation detection. Systematic evaluation reveals several notable gaps in current methods. In particular, current methods struggle to correctly link genes and proteins and often have difficulty effectively incorporating context into linking decisions. To expedite future development and baseline testing, we release our unified evaluation framework and all included models on GitHub at https://github.com/davidkartchner/biomedical-entity-linking. David Kartchner, Jennifer Deng, Shubham Lohiya, Tejasri Kopparthi, Prasanth Bathala, Daniel Domingo-Fernández, Cassie S. Mitchell |
EMNLP | 4 |