Mapping free-text mentions in clinical notes to standardized terminologies such as SNOMED CT is essential for large-scale secondary use of electronic health records, but remains challenging due to linguistic variability, under-specified annotation guidelines, term ambiguity, and ontology scale. This work presents a two-stage entity linking pipeline that combines span detection with context-aware concept linking and evaluates it on the SNOMED CT Entity Linking Challenge dataset. Our work builds upon the SNOMED CT entity linking challenge (CITATION), resulting in a fully open-source system. To our knowledge, this is the first end-to-end open-source system for this task. For span detection, we compare multiple neural architectures together with dictionary-based matching. For concept linking, we adopt a context-aware bi-encoder, and construct a multi-source knowledge base enriched with context derived from the SNOMED CT ontology. Finally, we implement an agentic re-ranker and test the effectiveness of LLM-backed re-ranking with access to annotation guidelines. In contrast to findings from the original shared task submissions, we show that context is important for optimal performance, and that agentic re-ranking with a state-of-the-art LLM only marginally improves overall performance, suggesting that the current benchmark may be approaching its practical ceiling. This work provides the first fully open-source, reproducible system for SNOMED CT entity linking, offering a foundation for future research and practical deployment.

This paper describes our submission for the BioNLP ACL 2025 Shared task on grounded Question Answering (QA) from Electronic Health Records (EHRs). The task aims to automatically generate answers to patients’ health related questions that are grounded in the evidence from their clinical notes. We propose a two stage retrieval pipeline to identify relevant sentences to guide response generation by a Large Language Model (LLM). Specifically, our approach uses a BioBERT based bi-encoder for initial retrieval, followed by a re-ranking step using a fine-tuned cross-encoder to enhance retrieval precision. The final set of selected sentences serve as an input to Mistral 7B model which generates answers through few-shot prompting. Our approach achieves an overall score of 31.6 on the test set, outperforming a substantially larger baseline model LLaMA 3.3 70B (30.7), which demonstrates the effectiveness of retrieval-augmented generation for grounded QA.

This paper describes our system for the submission to the TextGraphs 2022 shared task at COLING 2022: Natural Language Premise Selection (NLPS) from mathematical texts. The task of NLPS is about selecting mathematical statements called premises in a knowledge base written in natural language and mathematical formulae that are most likely to be used to prove a particular mathematical proof. We formulated this task as an unsupervised semantic similarity task by first obtaining contextualized embeddings of both the premises and mathematical proofs using sentence transformers. We then obtained the cosine similarity between the embeddings of premises and proofs and then selected premises with the highest cosine scores as the most probable. Our system improves over the baseline system that uses bag of words models based on term frequency inverse document frequency in terms of mean average precision (MAP) by about 23.5% (0.1516 versus 0.1228).

The paper describes our submissions for the Social Media Mining for Health (SMM4H) workshop 2022 shared tasks. We participated in 2 tasks: (1) classification of adverse drug events (ADE) mentions in english tweets (Task-1a) and (2) classification of self-reported intimate partner violence (IPV) on twitter (Task 7). We proposed an approach that uses RoBERTa (A Robustly Optimized BERT Pretraining Approach) fine-tuned with a label distribution-aware margin loss function and post-hoc posterior calibration for robust inference against class imbalance. We achieved a 4% and 1 % increase in performance on IPV and ADE respectively when compared with the traditional fine-tuning strategy with unweighted cross-entropy loss.