24 citations · 31 across the 9 of their papers we have counts for
5 papers · 1 filter
PubMedCausal: A Span-Level Annotated Corpus for Causal Relation Extraction in Biomedical Text
Ifeoluwa Kunle-John, Josiah Paul, Oluwatosin Agbaakin +3
Causal relation extraction (CRE) is central to biomedical text mining, but current resources often conflate causal relations with broader associations, restrict annotation to sente…
Benchmarking LLMs for Pairwise Causal Discovery in Biomedical and Multi-Domain Contexts
Sydney Anuyah, Sneha Shajee-Mohan, Ankit-Singh Chauhan +1
The safe deployment of large language models (LLMs) in high-stakes fields like biomedicine, requires them to be able to reason about cause and effect. We investigate this ability b…
Domain-Specific Knowledge Graphs in RAG-Enhanced Healthcare LLMs
Sydney Anuyah, Mehedi Mahmud Kaushik, Hao Dai +3
Large Language Models (LLMs) generate fluent answers but can struggle with trustworthy, domain-specific reasoning. We evaluate whether domain knowledge graphs (KGs) improve Retriev…
Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modelling
Sydney Anuyah, Mehedi Mahmud Kaushik, Krishna Dwarampudi +3
We introduce CoDe-KG, an open-source, end-to-end pipeline for extracting sentence-level knowledge graphs by combining robust coreference resolution with syntactic sentence decompos…
An Empirical Study of Causal Relation Extraction Transfer: Design and Data
Sydney Anuyah, Jack Vanschaik, Palak Jain +2
We conduct an empirical analysis of neural network architectures and data transfer strategies for causal relation extraction. By conducting experiments with various contextual embe…