arXiv AI By Yixuan Tang, Zhenghong Lin, Yandong Sun, Wynne Hsu, Mong Li Lee, Anthony K. H. Tung

Asking the Right Questions: Ontology-Grounded Interpretable Embeddings for Biomedical Text

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arXiv AI
Jul 15

CANDI: Contextual Alignment for Niche Domains Question Answering

arXiv:2607. 11891v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) in specialized domains like medical diagnostics and financial advisory necessitates evaluating capabilities beyond general knowledge.

By Megha Chakraborty, Darssan L. Eswaramoorthi, Het Riteshkumar Shah, Madhur Thareja, Michelle A Ihetu, Harshul Raj Surana, Kaushik Roy, Amit Sheth
arXiv Machine Learning
Aug 6

Neighborhood-Aware Dual Biomedical Entity Linking

arXiv:2608. 04144v1 Announce Type: cross Abstract: Biomedical entity linking grounds mentions in clinical and scientific text to entities in a curated knowledge base (KB) with ontological structure, which supports downstream applications such as literature-scale information extraction and patient-record normalization.

By Yicheng Tao, Jie Liu
arXiv AI
Sep 3

BioELX: Context-Aware Cross-lingual Biomedical Entity Linking without Task-Specific Supervision

BioELX is a retrieve‑rerank framework for cross‑lingual biomedical entity linking that tackles two key problems: the English‑biased UMLS alias training data and the degradation caused by naïvely adding context. It fine‑tunes SapBERT_multi with Wikidata‑derived cross‑lingual alias supervision to create shared concept neighborhoods, and then reranks candidates using pretrained LLMs with mention‑anchored prompting to focus on the target mention. Experiments demonstrate state‑of‑the‑art performance on four benchmarks, improving Recall@1 by 4.8–18.2 percentage points without task‑specific annotations.

By Yi Wang, Corina Dima, Liangyu Zhong, Steffen Staab