40 citations · 119 across the 19 of their papers we have counts for
19 papers
A Benchmark for End-to-End Zero-Shot Biomedical Relation Extraction with LLMs: Experiments with OpenAI Models
Aviv Brokman, Xuguang Ai, Yuhang Jiang +2
Extracting relations from scientific literature is a fundamental task in biomedical NLP because entities and relations among them drive hypothesis generation and knowledge discover…
Relation Extraction with Instance-Adapted Predicate Descriptions
Yuhang Jiang, Ramakanth Kavuluru
Relation extraction (RE) is a standard information extraction task playing a major role in downstream applications such as knowledge discovery and question answering. Although deco…
Knowledge-Driven Cross-Document Relation Extraction
Monika Jain, Raghava Mutharaju, Kuldeep Singh +1
Relation extraction (RE) is a well-known NLP application often treated as a sentence- or document-level task. However, a handful of recent efforts explore it across documents or in…
How Important is Domain Specificity in Language Models and Instruction Finetuning for Biomedical Relation Extraction?
Aviv Brokman, Ramakanth Kavuluru
Cutting edge techniques developed in the general NLP domain are often subsequently applied to the high-value, data-rich biomedical domain. The past few years have seen generative l…
Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction
Monika Jain, Raghava Mutharaju, Ramakanth Kavuluru +1
Document-level relation extraction (DocRE) poses the challenge of identifying relationships between entities within a document as opposed to the traditional RE setting where a sing…
Comparison of pipeline, sequence-to-sequence, and GPT models for end-to-end relation extraction: experiments with the rare disease use-case
Shashank Gupta, Xuguang Ai, Ramakanth Kavuluru
End-to-end relation extraction (E2ERE) is an important and realistic application of natural language processing (NLP) in biomedicine. In this paper, we aim to compare three prevail…