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.

Machine learning and deep learning models have shown great potential in detecting hate speech from social media posts. This study focuses on the homophobia and transphobia detection task of LT-EDI-2024 in English. Several machine learning models, a Deep Neural Network (DNN), and the Bidirectional Encoder Representations from Transformers (BERT) model have been trained on the provided dataset using different feature vectorization techniques. We secured top rank with the best macro-F1 score of 0.4963, which was achieved by fine-tuning the BERT model on the English test set.

We describe work from our investigations of the novel area of multi-modal cross-lingual retrieval (MMCLIR) under low-resource conditions. We study the challenges associated with MMCLIR relating to: (i) data conversion between different modalities, for example speech and text, (ii) overcoming the language barrier between source and target languages; (iii) effectively scoring and ranking documents to suit the retrieval task; and (iv) handling low resource constraints that prohibit development of heavily tuned machine translation (MT) and automatic speech recognition (ASR) systems. We focus on the use case of retrieving text and speech documents in Swahili, using English queries which was the main focus of the OpenCLIR shared task. Our work is developed within the scope of this task. In this paper we devote special attention to the automatic translation (AT) component which is crucial for the overall quality of the MMCLIR system. We exploit a combination of dictionaries and phrase-based statistical machine translation (MT) systems to tackle effectively the subtask of query translation. We address each MMCLIR challenge individually, and develop separate components for automatic translation (AT), speech processing (SP) and information retrieval (IR). We find that results with respect to cross-lingual text retrieval are quite good relative to the task of cross-lingual speech retrieval. Overall we find that the task of MMCLIR and specifically cross-lingual speech retrieval is quite complex. Further we pinpoint open issues related to handling cross-lingual audio and text retrieval for low resource languages that need to be addressed in future research.